Challenges of Community Reentry for the Geriatric Inmate Population of Onitsha Correctional Centre, Anambra State

Citation

Ume, I. S., Onwuchekwe, S. I., Onuchukwu, G., & Obi, C. C. (2026). Challenges of Community Reentry for the Geriatric Inmate Population of Onitsha Correctional Centre, Anambra State. Think India Quarterly, 29(2), 1–13. https://doi.org/10.26643/rb.v118i11.10705

Ignatius SundayUme^1;Si.ume@coou.edu.ng; https://orcid.org:000900618459479,

Stanley IkennaOnwuchekwe²; si.onwuchekwe@coou.edu.ng: https://orcid.org/0009

0004-0330-1770,

Greg Onuchukwu³_;greg.onuchukwu@federalpolyoko.edu.ng,https://orcid.org: 0009-0005-1713-9855,

Charity Chioma Obi⁴; charity.obi@federalpolyoko.edu.ng:https://orcid.org: 0009_0003_1770_6894

^1, Department of Sociology and Anthropology, Chukwuemeka Odumegwu Ojukwu University, Igbariam, Anambra State, Nigeria

^2, Department of Criminology and Security Studies, Chukwuemeka Odumegwu Ojukwu University, Igbariam, Anambra State, Nigeria

^3-4,Department of Social Sciences, School of General Studies^,Federal Polytechnic Oko, Anambra State, Nigeria

Corresponding Author: Stanley Ikenna Onwuchekwe, email; si.onwuchekwe@coou.edu.ng ORCID: https://orcid.org/0009-0004-0330-1770, Chukwuemeka Odumegwu Ojukwu University, Anambra State, Nigeria

Abstract

Geriatric inmates upon release form the correctional center face severe community reentry challenges, driven primarily by profound social stigma and exclusion, family abandonment, poor health, and lack of financial resources. This study examines Challenges of Community Reentry for the Geriatric Inmate Population of Onitsha Correctional Centre, Anambra State. The research adopted reintegration theory as theoretical framework. The study revealed that geriatric inmates face significant community re-entry barriers which include; stigmatization, homelessness, poor access to healthcare, lack of social and financial support, etc. The study concludes that without deliberate, age-specific interventions, geriatric inmates upon release from correctional centres are likely to face serious challenges which will make their re-entry into their community a herculean task for them. It therefore recommends that the Nigerian Correctional Service should establish transitional housing schemesand geriatric-specific healthcare access programs for geriatric inmates to address the pressing issues of homelessness and medical neglect post-release, amongst others.

Keywords; Community Reentry, Geriatric Inmate, Ex-offenders, Prisons, Correctional service

1. INTRODUCTION

Every constituted body is made with a general and specific function in mind, and the relevance of such body is always measured by its ability to fulfill its expected role (Onwuchekwe, Okafor & Madu, 2020). The prison system (or correctional service system) is a crucial component of a nation’s penal institutions, serving as the primary mechanism for securely confining individuals who have been convicted of crimes or are awaiting trial (Ajah&Nweke, 2017). It is a critical segment of the criminal justice system (Aboki, 2007). Ideally, the aim of putting somebody in prison or correctional centre among others is to help the person imbibe new ways of life, hopefully to get reintegrated into society. Prisons or correctional centers are therefore designed to provide a secure and safe place for individuals who have been convicted of a crime, with the hope of rehabilitation and reintegration into society. Historically, imprisonment has evolved as a more humane alternative to other forms of punitive measures, accommodating offenders within structured environments designed for behavioral correction (Feral, 2002). Modern prisons according to Giddens (1991) have their origins not in the jails and dungeons of former times but in workhouses (often referred to as “hospitals’’). However despite a prison’s intended rehabilitative function, Idowu and Muhammed (2019); Mohammad (2017) identifies several challenges affecting correctional centers in Nigeria. These include insufficient feeding, inadequate rehabilitation facilities and programs, poor working conditions, overcrowding/congestion, and the failure to separate inmates based on their specific needs. Also, lack of proper planning and provision for geriatric inmates is another big challenge that faces the correctional centers in Nigeria. Most correctional centers in Nigeria are designed only for young and active inmates. For most geriatric inmates they struggle to copy with difficulties in the correctional centres such as long distances, the stairs, top bunks, and dimly lit, cold, or damp environments.

No doubt the challenges faced by geriatric inmates have become an impediment to their full and proper reentry into their communities. Davis et al (2012) noted that the prison environment is markedly different from mainstream society. Therefore, when being released, ex-convicts are plunged into an environment that is quite different from that of the prison and they struggle to cope. Furthermore, given the dynamic and ever-changing nature of society, ex-offenders who spend long periods in prison are released into an environment that is very different from their former environment before imprisonment. This appears to pose a serious challenge for their smooth reintegration process ((Onwuchekwe, Ibekwe, Ezeh, &Okpala, 2023). Osayi (2015) noted that Nigerian prison has proved dysfunctional, because rather than reconciling the offender with the social order and its laws, the prison has been a center for the dissemination and exchange of criminal influences and ideas, and has usually rendered the prison processed offenders unable to re-integrate into the society.

The reentry of geriatric inmates into the community presents a more complex challenge due to their advanced age, declining health, and lack of social and financial support (Ajah&Nweke, 2017). Also, most geriatric offenders suffer from community rejection upon release from correctional center. According to Lindsey and Beach (2002), individuals do not respond to their environment rather, they respond to the meanings to which they ascribe to social events through their collective sharing of meanings through symbols. Through human interactions within their milieus, they determine what is important and what is not important for them (Nwosu, Abunike, Onwuchekwe &Onuchukwu, 2022). When individuals have the perception that ex-offenders are criminals, they tend to be more reserved in dealing and accommodating them within their environment. Most geriatric inmates upon release form the correctional center face severe community reentry challenges, driven primarily by profound social stigma and exclusion, family abandonment, poor health, and lack of financial resources. In fact, the reintegration of discharged geriatric offenders is often hindered by community perceptions of them as unrepentant criminals. Most of them are denied decent accommodation even in their family houses leading to homelessness. For Onwuchekwe et al., (2023) the manner in which marginalized members of a society is perceived or treated in social interaction seems to shape their wellbeing and subsequent actions. Most geriatric inmates often perceived as evildoers by community members face challenges of accessing genuine community-based support and social welfare upon release from correctional centre. Some of them suffer from loss of familial ties which leaves them isolated and abandoned. There are also instances where some of them are denied access to their personal assets, which has equally resulted cases of extreme poverty among them. Ajah&Nweke (2017) observes that the reintegration challenges faced by ex-convicts are largely shaped by the perceptions held by communities and society at large, which significantly hinder their ability to secure employment post-incarceration. In Nigeria today, it is common practice for employers to discriminate against individuals with prior criminal convictions, thereby reducing their chances of securing stable employment.

Bebbington et al., (2021) noted that recently released offenders suffer from negative mental health effects due to a lack of a support system and the resources required for reintegration into the community. Geriatric offenders often grapple with severe health challenges, including chronic illnesses, physical disabilities, and cognitive impairments. Studies reveal that approximately 40% of incarcerated individuals aged 55 and above suffer from cognitive impairments, making it difficult for them to navigate post-incarceration life without structured support. The lack of accessible healthcare services upon release further compounds their struggles, leaving them vulnerable to health deterioration, depression, and premature mortality. The case of the Onitsha Correctional Centre in Anambra State highlights the urgent need for structured reintegration programs tailored to the geriatric inmate population. Many geriatric inmates face severe barriers in accessing post-release support services. Given these challenges, there is a critical need to assess the existing community reentry mechanisms and develop policies that will address the unique needs of geriatric inmates.

2. Conceptual Framework

Concept of Geriatric Inmate

According to the Australian Institute of Criminology (AIC) (2011), a functional criterion for older incarcerated adults is 50 years of age or older. Grant (1999) and Hayes et al., (2012) posited that ageing is thought to begin at 50 in the prison population as opposed to 60 in the general population. Human Rights Watch (2012) noted that prison life may be difficult for everyone, but it can be especially difficult for those whose bodies and brains may be affected by changes associated with ageing and may depend more on others and may lose some or all of their autonomy due to ageing. Help Age International (2011) asserts that as we age, our rights do not alter. In addition, as people age, they encounter greater obstacles to involvement, depend more on others, and lose some or all of their autonomy. A geriatric inmate is referred to as an incarcerated person who experiences accelerated aging due to poor health, lifestyle, and the harsh conditions of prison confinement. Geriatric inmates often have higher rates of chronic illnesses (hypertension, diabetes, heart disease) compared to their younger counterparts and the general population. They frequently suffer from geriatric syndromes such as cognitive impairment/dementia, mobility issues, incontinence, falls, and sensory loss (hearing/vision). Older incarcerated persons also experience isolation and prejudice because their unique medical, social, and educational requirements are not being served (Prison Reform Trust, 2011).

Olaoye (2025) observed that apart from a strong indication of an increasing number of elderly prisoners, there is strong evidence that geriatric inmates in correctional institutions are exposed to a high burden of physical and mental health problems. Up to 90% have at least one moderate or severe medical condition, with more than 50% having three or more forms of health condition (Public Health England, 2017; Olaoye, 2025). Onwuchekwe, et al (2023) noted that irrespective of the circumstances that surround social existence of certain individuals, all human beings aspire to live a fulfilling, satisfying and meaningful life. The author argued that offenders released from correctional institutions could sometimes be confronted by socio-cultural, economic and personal challenges that tend to become obstacles to a crime free lifestyle and re entry process. Some of these challenges might be as a result of the consequences of incarceration and the difficulty of transiting back into the community (Ajala & Oguntuase, 2011; Onwuchekwe, et al, 2023). Ossayi (2015) noted that in Europe and America, a number of after-care initiatives such as Reintegrative Confinement, Structured Transition, Intensive After-care, and Community Correction which include Halfway Houses, Furloughs, Probation and Parole have been developed and implemented to ease the transition problems of released offenders. In Nigeria, the author argues that only while lip-service is paid to the existence of after-care services, also, provision for community based corrections is apparently not in existence.

The issue of geriatric inmates in correctional facilities has emerged as a significant concern, as the aging prison population continues to rise. Many correctional institutions were originally designed for younger offenders, leaving elderly inmates in environments that do not cater to their specific needs. Research highlights the growing medical, psychiatric, and social challenges that this population faces, as well as the policy implications and potential solutions to address these issues. It is concerning that older inmate in Nigeria correctional centers face difficult reentry challenges compared to their younger counterparts. Many geriatric ex-offenders are released into communities without access to stable housing, making them highly vulnerable to homelessness. Research indicates that formerly incarcerated individuals are ten times more likely to experience homelessness compared to the general population, with older adults being at an even higher risk. Asokhia and Agbonluae, 2013; Chukwudi (2012) observed that in Nigeria, social welfare systems are limited; the absence of structured reintegration programs exacerbates the struggles of elderly ex-inmates. Many of them lack financial resources, making it difficult to secure accommodation or afford basic needs upon release.

Concept of Community Re-entry

According to Okah et al. (2024) community re-entry is the process of facilitating a transition or movement of an offender who has completed their sentence, and rehabilitation programs in a correctional institution back to their family, environment, and community where they belong. Community reentry is the process by which ex-convicts transition back into society and gain acceptance from key stakeholders, including families, employers, and communities (Idowu&Odivwri, 2019). For Laub & Sampson (2003) community reintegration is the process of transitioning from incarceration to the community, adjusting to life outside of prison or jail, and attempting to maintain a crime-free lifestyle. Community reentry is frequently described as reintegration because it involves preparing not only the ex-offender but also the family, community, and victims for the transition process (Stravinskas, 2009). It is one of the most important indicators that determine the success of previously incarcerated individuals’ rehabilitation. It contributes to helping one’s adaptation to life adversities in the society.

Shajobi-Ibikunle (2014) and Aniekan (2016) observed that the common perception among communities is that little or nothing could be done to rehabilitate or change the behaviour of ex-offenders who they see as dangerous individuals. Thus, formerly incarcerated individuals face significant challenges during community re-entry. These barriers include stigma, difficulty in finding employment, limited access to housing, and lack of educational opportunities (Arevalo, 2020). Many re-entering individuals struggle to access quality re-entry programs, particularly those that address substance abuse and mental health needs. The financial burden associated with reintegration is also a major obstacle, disproportionately affecting marginalized groups, including people of colour and women. Social networks and family relationships further complicate re-entry, as individuals with a history of incarceration often experience strained relationships with loved ones, which can impact their emotional and financial stability (Weill, 2016). In Nigeria; prisoners are often released without adequate preparation for life outside the prison system. Upon release, they are left to find housing, employment, and basic necessities on their own, often with little to no support. Many ex-convicts experience isolation and alienation due to the absence of transitional case managers who could guide them through this critical period. As a result, they struggle to rebuild their lives and frequently resort to crime out of necessity (Petersilia, 2003; Stravinskas, 2009).

Studies highlight the need for comprehensive discharge planning that includes mental health services, substance abuse treatment, and access to healthcare (Luther et al., 2011). Without proper support, many individuals return to behaviors that led to their incarceration in the first place, increasing their risk of recidivism. According to Iremeka, F.U., Eseadi, C., Ezenwaji, C. et al (2021) rational emotive-behaviour therapy (REBT) has shown great promise in helping students manage mental distress. Such therapy can as well be adopted to address the need of geriatric inmates. Also, programs that integrate healthcare services with re-entry planning have been shown to improve long-term outcomes by addressing the root causes of criminal behavior and providing individuals with the tools they need to reintegrate successfully.

Theoretical framework

Reintegration Theory

Reintegration theory focuses on the process of re-entering individuals primarily ex-offenders back into society by restoring their social, economic, and psychosocial ties. It emphasizes a transition from a marginalized status to civilian or law-abiding life, requiring community acceptance and the reduction of stigma to lower recidivism rates. Muntingh (2005) noted that the rationale for reintegrating offender is based on two moral premises. Firstly, it is better for people to be in harmony with one another in their community, and secondly, wherever harmony and community are absent, they should be actively pursued. The author further noted that punitive approach stigmatises and belittles offenders. This results in a further breach of community and disruption of harmony in society. To this end, reform and reintegration of offenders should always be the ultimate aim of incarceration. In application therefore, reintegration theory tries to point to societal role in crime perpetration and dissuade the blame game of the community. It perceives the society as an accomplice in crime commission and therefore must help in treating and rehabilitating the offenders, especially in ensuring that they reintegrate successfully (Onwuchekwe et al, 2023).

Some of the conditions that breed criminals whom many societies create are discrimination against ex-convicts by community members and the assumption that upon released from correctional facilities, the ex-convicts may still go back to reoffending. Many geriatric inmates upon release from correctional centres suffer from community avoidance and stigmatization. The sense of not being welcomed anymore as part and parcel of their community depresses them the more. Therefore, for reintegration theorists, communities should be open minded and show willingness to welcome geriatric inmates back without any form of reservation. They argue that it is only through this that the gains of rehabilitation received by at the correctional service centres would be sustained.

Conclusion and recommendation

Geriatric inmates encounter significant community reentry challenges upon release from correctional center due to family and community abandonment, rejection and stigmatization, etc. They are most often stereotyped, labeled, and discriminated against by their own family and community. The stigmas they suffer most times erode their self-esteem and weaken their social cohesion. In most communities in Nigeria, ex-geriatric offenders are most often judged by their past crimes by community members. They are rejected and excluded from participating in key community activities. Most of them are pushed to the margins of society, unable to meet basic survival needs upon their release from the correctional centre. Ahmed (2015) further supports this, noting that harsh prison conditions and societal rejection create a cycle where ex-inmates, especially the vulnerable ones. Indeed, most elderly ex-inmates lacked the necessary support to be able to integrate proper into their community.

Most geriatric inmates of Onitsha correctional centre often leave the centre without having accessed any meaningful training or rehabilitation that will them integrate into their community. Although this study found that some reintegration programs actually exist, their impact on geriatric inmates is moderate and uneven. Idowu and Odivwri (2019) shares this concern in their study which found that Nigerian correctional facilities often fall short in delivering true rehabilitation, leading to high recidivism rates. Indeed, the geriatric inmates are often overlooked when reintegration services are designed by correctional service centers in Nigeria. They do not actually benefit because most of the programs target younger or more able-bodied inmates. Many of geriatric inmates who need healthcare navigation, housing assistance, and psychological support usually don’t get them. In the light of the above, this study concludes that without deliberate, age-specific interventions, geriatric inmates upon release from correctional centres are likely to face serious challenges which will make their proper re-entry into their community a herculean task for them.

Therefore, this paper recommends that:

1. The Nigerian Correctional Service should establish transitional housing schemes and geriatric-specific healthcare access programs for elderly ex-inmates to address the pressing issues of homelessness and medical neglect post-release.

Correctional facilities like Onitsha should revamp their rehabilitation approach by incorporating age-sensitive vocational training, counseling, and reentry planning that begins early in incarceration and continues post-release, specifically designed for elderly inmates.
Communities should be sensitized to accept ex-geriatric offenders back without reservations of any kind.
Policymakers should consider adopting a National Geriatric Reintegration Strategy (NGRS) that will target interventions such as micro-grants for ex-geriatric inmates and community reentry programs that will pair ex- ex-geriatric inmates with trained community volunteers.

References

Aboki, E. (2007). History of Nigeria Prisons Service: An Inside Account. Kaduna: Bizmak Publishers.

Ajah, B. O., &Nweke, J. O. (2017). Prison facilities and the welfare of inmates in Nigeria: A study of Abakaliki and Awka prisons. World Applied Sciences Journal, 35(3), 361-369.

Ajala, E. M., &Oguntuase, A. E. (2011). Effectiveness of after-care-services in the reintegration and supply of labour after incarceration in the prisons: Lagos and Oyo States Prison Command Experience. African Research Review, 5(5), 185-198.

Aniekan, S. B. (2016). Ex-convicts in a dilemma of reintegration: A study of Uyo, South-South Nigeria. International Journal of Social Sciences, 10(4), 81-91.

Arevalo, S. P. (2020). Barriers to Reentry Following Incarceration (Doctoral dissertation, California State University, Northridge).

Asokhia, M. O., &Agbonluae, O. O. (2013). Assessment of rehabilitation services in Nigerian prisons in Edo State. American International Journal of Contemporary Research, 3(1), 224-230.

Australian Institute of Criminology, Older prisoners (2011). A challenge for Australian corrections, Trends & Issues in Crime and Criminal Justice No 426, 1 – 10

Berghuis, M. (2018). Reentry programs for adult male offender recidivism and reintegration: A systematic review and meta-analysis. International Journal of Offender Therapy and Comparative Criminology, 62(14), 4655-4676.

Brown, A. S. (2016). Ex-convicts in a dilemma of reintegration: A study of Uyo, South-South Nigeria. International Journal of Social Sciences, 10(4), 81-91.

Chukwudi, F. (2012). Challenges of reforms in the Nigerian prison system: Lessons from USA and South Africa. Journal of social science and public policy, 4(9), 35-46.

Chukwu, C.C., Ume, I.S. and Dibia, S.B. (2018), “The role of neo-patrimonialism in elections and the challenge of national security in contemporary Nigerian society: An appraisal”, International Journal of Development and Sustainability, Vol. 7 No. 6, pp. 1800-1814

Davis, C., Bahr, S. J., & Ward, C. (2012). The process of offender reintegration: Perceptions of what helps prisoners reenter society. Criminology and Criminal Justice, 13(4), 446–469.

Egboh, F.A., and Nweke, G. I.(2020) National Security and Crime in Era Of Covid-19 in Nigerian Major Cities: Beyond A Lockdown, IGWEBUIKE: An African Journal of Arts and Humanities, Vol. 6. No. 5. ISSN: 2488- 9210 (Print) 2504-9038 (Online) 2020. Department of Philosophy and Religious Studies, Tansian University

Feral, S. (2002).Rethinking What Works with Offenders: Probation, Social Content and Desistence Form Crime. Devon: William Publishing.

Grant, A. (1999) ‘Trends & Issues in Crime and Criminal Justice”. Canberra: Australian Institute of Criminology. No 115, <http://www.aic.gov.au/publications/currentseries/tan-di/101-120/tandi115.aspx>.

Help Age International (2011). Submission for the consultation on the Human Rights of Older Persons Follow-up to the Second World Assembly on Ageing. Retrieved February 21,2023.

Human Rights Watch. (2012). Old behind bars: The aging prison population in the United States

Hayes, A. J., Burns, A., Turnbull, P. & Shaw, J. J. (2012). The health and social needs of oldermale inmates. International Journal of Geriatric Psychiatry 27, 1155 – 1162

Idowu, O. A., &Odivwri, E. J. (2019). Re-integration of released offenders: A panacea to vicious circle of recidivism in Ondo State, Nigeria. Gouni Journal of Faculty of Management and Social Sciences, 7(1), 32-51.

Iremeka, F.U., Eseadi, C., Ezenwaji, C. et al. Effect of School-based Rational-Emotive Behaviour Program on Burnout Among Adult Learners: Moderating Influence of Participants’ Demographic Variables. J Rat-Emo Cognitive-Behav Ther 39, 712–729 (2021). https://doi.org/10.1007/s10942-021-00393-4

Mohammad, Y. (2017). The Roles of Correctional Institution (Remand Home) in Controlling Juvenile Delinquency in Kakuri, Kaduna-South LGA, Kaduna State, Nigeria (Unpublished B.Sc. Project). Department of Sociology.

Muntingh, L. (2005). Offender Rehabilitation and Reintegration: Taking the White Paper on Corrections Forward. Cape Town: Civil Society Prison Reform Initiative.

Murolo, A. S. (2020). Geriatric inmates: Policy and practice. Journal of Correctional Health Care, 26(1), 4-16.

Nwosu, K.U., Abunike, U.E., Onwuchekwe, S. I., & Greg Onuchukwu, G.(2022) Police Accountability in the Post-ENDSARS Protest: Perceptions of Youths in Southeast, Nigeria, International Journal of Health and Social Inquiry, Vol. 8, No.1, 2022

Nwune, E. C., Ajah, B. O., Egbegi, F. R., &Onyejegbu, D. C. (2019). Across the wall: The perception of rehabilitation, reformation and reintegration programmes in Anambra State Prison Command. Journal of Law and Judicial System, 2(2), 13-22.

Ojha, M. U., Pape, C. M., &Burek, M. W. (2018). Reentry in a comparative context: Exploring past, present, and future participation in services between nonurban and urban inmates. The Prison Journal, 98(2), 163-187.

Okunola, R. A., Aderinto, A. A., &Atere, A. A. (2002). The Prison as a Social System. In U. C. Isuigo-Abanihe, A. N. Isamah, & J. O. Adesina (Eds.), Currents and Perspectives in Sociology (pp. 319-333). Lagos: Malthouse Press

Onwuchekwe,S.1., Ibekwe, C.C., Ezeh, S.S., & Okpala, F.C (2023) Public Perception of Ex-Convicts Reintegration in Idemili-North Local Government Area of Anambra State, Nigeria, Journal of Law and Judicial System 2023;6(1): 15-29 https://doi.org/10.22259/2637-5893.0601002

Onwuchekwe, S.I, Okafor, F. C. and Madu, E.T (2020) A Perception Study Of The Niger-Delta Community On An ECOWAS Role In The Niger Delta Security Crisis, Nigerian Journal of African Studies, Vol. 2 No. 2, 2020 (ISSN: 2734

Osayi, K. K. (2015). Socio-cultural factors affecting reintegration of discharged prisoners in Anambra State, South East, Nigeria. International Journal of Social Science and Humanity, 5(1), 39-

Otu, M. S. (2015). Analysis of the causes and effects of recidivism in the Nigerian prison system. International Journal of Development and Management Review, 10(1), 136-145.

Petersilia, J. (2003). When Prisoners Come Home: Parole and Prisoner Reentry. Oxford University Press.

Ruggero, T., Dougherty, J., &Klofas, J. (2015). Measuring Recidivism: Definitions, Errors, and Data Sources. Working Better: Centre for Public Safety Initiatives.

Schneider, C. R., & Weber, E. U. (2020). Reducing discrimination and fostering prosociality towards ex‐prisoners in Nigeria and the United States. Journal of Social Issues, 76(1), 172-199.

Shajobi-Ibikunle, G. D. (2014). Challenges of imprisonment in the Nigerian penal system: The way forward. American Journal of Humanities and Social Sciences, 2(2), 17-25.

Stravinskas, S. (2009). Lower Crime Rates and Prisoner Recidivism (Honors College
Thesis). Department of Criminal Justice, Pace University.

Travis, J. (2005). But they all come back: Facing the challenges of prisoner reentry. The Urban Institute.

Ume, I. S., & Chukwu, C.C (2019) Democracy And Sustainable Development In Nigeria: Challenges And Prospects In The Niger Delta, GSJ: Volume 7, Issue 9, September 2019 ISSN 2320-9186

Ugwoke, C. U., &Ameh, O. S. (2014). Rehabilitation of convicts in Nigerian prisons: A study of federal prisons in Kogi State. Journal of Humanities and Social Sciences, 4(26), 33–42.

United Nations Office on Drugs and Crime. (2006). Criminal Justice Assessment Toolkit, Organization for Cooperation and Security in Europe. UN, Vienna – New York. Available [Online] at: http://www.unodc.org/pdf/criminal_justice/INTERNATIONAL_COOP.pdf.

Visher, C. A. (2015). Re-entry and Reintegration after Incarceration. In: International Encyclopedia of the Social &Behavioral Sciences. 2nd edn. Oxford: Elsevier, pp. 61–66.

Visher, C. A., Winterfield, L., &Coggeshall, B. (2005). Ex-offenders Employment Programmes and Recidivism: A Meta-analysis. Journal of Experimental Criminology, 1(3), 295-315.

Walmsley, R. (2003). Global Incarceration and Prison Trends. Forum on Crime and
Society, 3(1&2), 65-78.

Williams, B., &Abraldes, R. (2007). Growing older: Challenges of prison and reentry for the aging population. In Public health behind bars: From prisons to communities (pp. 56-72). New York, NY: Springer New York.

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Artificial Intelligence Technologies and the Control of Oil Theft in Warri South-West Local Government Area of Delta State, Nigeria

Citation

Onwuchekwe, S. I., Ibekwe, C. C., Ume, I. S., Agbodike, M. C., & Onuchukwu, G. (2026). Artificial Intelligence Technologies and the Control of Oil Theft in Warri South-West Local Government Area of Delta State, Nigeria. International Journal for Social Studies, 12(2), 1–20. https://doi.org/10.26643/ijss/6

Stanley Ikenna Onwuchekwe

Department of Criminology and Security Studies,

Chukwuemeka Odumegwu Ojukwu University,

Igbariam, Anambra State, Nigeria

Email: si.onwuchekwe@coou.edu.ng:

https://orcid.org/00090004-0330-1770,

Ibekwe, Christopher Chimaobi

Department of Sociology, Faculty of Social Sciences,

Ambrose Alli University, Ekpoma, Edo State, Nigeria

Email: ccibekwe@aauekpoma.edu.ng

Ignatius SundayUme

Department of Sociologyand Anthropology, Faculty of Social Sciences,

Chukwuemeka Odumegwu Ojukwu University, Igbariam, Anambra State

Email^:Si.ume@coou.edu.ng; https://orcid.org:000900618459479

Agbodike, Mmesoma Chinecherem

Department of Criminology and Security Studies,

Chukwuemeka Odumegwu Ojukwu University,

Igbariam, Anambra State, Nigeria

Email: mc.agbodike@coou.edu.ng https://orcid.org/0009-0005-1182-1714

Greg Onuchukwu

Department of Social Sciences, School of General Studies^,

Federal Polytechnic Oko, Anambra State, Nigeria

Email: greg.onuchukwu@federalpolyoko.edu.ng

https://orcid.org: 0009-0005-1713-9855

Abstract

Oil theft in Nigeria has been a daunting challenge to meeting the approved 1.71 million barrels production per day and has led to the loss of over ten billion US dollars in foreign earnings. This paper examined artificial intelligence technologies and the control of oil theft in Warri South-West LGA of Delta State, Nigeria. Queer ladder theory was employed in explaining the complex dynamics around oil theft in the area. Mixed-methods research design was adopted. The target population was 22,234 and the sample size is 1,250 residents. This is in addition to five interviews that were conducted. Data were collected using structured questionnaire and in-depth interviews (IDI) guide. Quantitative data were analysed using percentage, frequency, charts, and multi-nominal logistic regression, while qualitative data were thematically analysed. Findings revealed that there was a high level of awareness amongst residents on AI enabled technologies used in controlling oil theft in their communities. It also showed that AI-powered devices, such as drones, satellites, CCTV and community-based mechanisms were used in the control of oil theft in the area. It equally indicated that these technologies are potentially useful, but their application was inadequate, leaving respondents skeptical of their effectiveness. It again showed that there was no significant positive relationship between respondents’ occupational group and their awareness of AI enabled technological tools for detecting oil theft. It concluded that application of AI technologies is sacrosanct in curbing oil theft, especially when synergized and blended with indigenous knowledge. Recommendations were made in line with the findings.

Keywords: Artificial intelligence, technology, oil theft, pipeline vandalisation.

Introduction

Nigeria is one of the largest oil producing countries in the world, yet it faces daunting challenges of crude oil theft. Criminal cartels and organized groups steal a substantial portion of the country’s daily oil production output. The country loses billions of dollars annually due to stolen oil, and it significantly affects national revenue and economic stability. Available statistics show that between January and July 2022, Nigeria lost an average of 437,000 barrels of crude oil to criminals on daily basis, which amounted to about 10 billion US dollars within the seven months period (Yusuf, 2022). This has equally resulted in the shortfall of supply of crude oil below the allotted 1.71 million barrels per day to Nigeria by the Organization of the Petroleum Exporting Countries (OPEC). The report further indicated that Nigeria was only producing about 50% of OPEC’s approved target for the country (Obiezu, 2022). This is not unconnected with the prevalence of oil theft in the oil producing communities in the Niger Delta.

The criminality associated with oil theft has indeed become a drain on Nigeria’s economy. Oil theft no doubt has triggered and perpetuated a circle of poverty and disillusionment among ordinary Nigerians, while enriching only a few elitists group and those involved in the illegal business. For most members of oil producing communities, oil resources are seen as a curse because their lives have not been impacted positively by it. There is lack of basic amenities such as pipe borne waters, electricity, good roads, and employment opportunities for teaming youth in the oil rich region (Akpan, Ufomba, Ibewke & Ufomba, 2017). Warri Southwest, which is part of the Niger Delta region is rife with civil unrest, militancy, social disorder, and disruption of the flow of crude oil supplies to illegal refineries, leading to production shortfall (Enuoh & Inyang, 2014). According to Onwuchekwe, Okafor and Madu (2020), the Federal Government of Nigeria has not been able to address most of the challenges dwindling the growth and prosperity of the area.

Evidence has shown that oil theft is not peculiar to any society, but a pervasive occurrence in many producing nations. In 2013, the Algerian Energy Authority reported losing US$1.3 billion a year as a result of fuel smuggling to neighboring countries (Al-Makhifi 2013). In 2015, during the Syrian civil war, the Islamic State of Iraq and Al-Sham (ISIS) made US$40 million a month from selling stolen crude oil to brokers. Some of the crude oil was refined into low-grade fuel and was smuggled into Turkey. ISIS sold most of its oil to the Assad regime, despite being its arch-enemy (Ralby, Ralby & Soud, 2017). This suggests a link between illegal access to oil and the funding of terrorism. Similarly, Russia’s state-owned investment bank, VTB Capital estimated that in 2013 the country’s oil companies were lost between US$1.8 to US$3.5 billion annually to oil theft (Khazov- Cassia, 2021). According to Ralby (2017), three million litres of fuel, valued at US$1.2 billion per annum are smuggled from Malaysia to Thailand through the land alone.

Onwuchekwe, Ezeah and Ikezue (2025) noted that oil deposits are found in different regions in Nigeria, but the issue of theft and lack of adequate control keep leading to losses. It does appear that the sustained oil theft syndrome in the Niger Delta, especially Warri Southwest is due to lack of suitable artificial intelligence (AI) enabled technologies. There is no doubt that lack of AI applications has emboldened oil thieves in the Niger Delta. Jia (2024) observed that non-deployment of modern and efficient technologies to achieve real-time aerial surveillance of oil facilities in the area have enabled oil theft to blossom. Similarly, Mallo (2024) noted that smart technologies, such as Fiber Optic Distributed Acoustic Sensing (DAS) are critical in detecting and classifying third party interference on oil installations. This suggests that AI enabled devices can actually prevent oil theft incidents before they occur.

A number of researches have been carried out on oil theft but none focused on issues of AI in the control of theft in Warri Southwest LGA, Delta State. While Eric and Oluwagbenga (2017) examined the impact of oil theft, illegal bunkering and pipeline vandalism on Nigeria’s economy between 2015 and 2016, Odalonu (2015) assessed the upsurge of oil theft and illegal bunkering in the region. Similar studies are either limited in contents, scope or forms, or centred on theoretical analysis of the issue. A preliminary scoping review on few academic databases including Science Direct, PubMed, Google Scholar and Web of Science showed that AI in the control of oil theft in Warri Southwest LGA, Delta State have not been reported in literature. This presents a gap in knowledge and it is against the backdrop that this paper examines artificial intelligence technologies and their application in the control of oil theft in Warri South-West LGA of Delta State, Nigeria.

Objectives of the Study

The broad objective of this paper is to examine artificial intelligence technologies and their application in the control of oil theft in Warri South-West LGA of Delta State, Nigeria. The specific objectives are;

To ascertain respondents’ level of awareness about AI enabled technologies in controlling oil theft in Warri Southwest LGA.
To identify AI surveillance technologies used in the control of oil theft in Warri Southwest LGA.
To ascertain respondents’ assessment of the effectiveness of AI enabled communication technologies in the control of oil theft in Warri Southwest LGA.

Hypothesis

There is a significant positive relationship between respondents’ occupational group and their awareness of AI enabled technological tools for detecting oil theft in Warri Southwest LGA.

Review of Theoretical Literature

Conceptualization of Oil Theft

Oil theft can be referred to in various terms as, oil bunkering, fuel scooping, and pipeline vandalism. It is a highly lucrative criminal activity, usually taking place in the creeks of the Niger Delta, where the pipelines are interconnected like a grid. This hazardous process involves illegally tapping into the pipelines and extracting crude oil (Onuoha, 2008). Crude oil theft, also known as illegal oil bunkering, is the illicit activity or unlawfully appropriating crude oil from pipelines or flow stations, as well as the unauthorized inclusion of additional crude oil into valid cargos without proper documentation or accountability (Asuni, 2009). Oil bunkering is commonly accomplished by breaching pipelines and tapping into them (Adegbite, 2013).

Crude oil theft is the unlawful carting away of crude oil or sabotaging of crude oil facilities through illegal bunkering, pipeline vandalism, fuel scooping, illegal refining and transportation and oil terrorism (Akpan et al, 2017). To Obasi (2011) oil theft in Nigeria is a generic term encompassing not only unauthorized loading of ships but also all acts involving diversion and smuggling of oil. A report by Stakeholders’ Democracy Network indicates that oil is being stolen at an industrial scale in the Niger Delta region (Boris, 2016).

According to Ayanruioh (2013:2), oil theft is the process through which crude oil or refined petroleum products are illegally siphoned from pipelines and sold to interested dealers / buyers waiting on the high sea or the unscrupulous individuals. Crude oil theft is often linked to organized crime, militant financing, and State corruption (NEITI, 2023).While traditionally analyzed as an economic crime, emerging studies emphasize its role in exacerbating insecurity (Obi, 2022). Unlike conventional theft, crude oil theft operates through a complex web involving political actors, security operatives, and transnational networks (Ibaba, 2021).

Furthermore, Saje and Abubakar (2023) conceptualized oil theft to mean illegal diversion and trade of either crude or refined petroleum oil by criminals for self-enrichment against the judicious exploitation of oil for revenue generation by the government. Romsom (2022) noted that these thefts are sometimes in small-scale, but the high profits in combination with the ability to penetrate multiple parts of the supply network, create incentives for criminals to expand their operations.

Artificial Intelligence Technologies and Oil Theft

AI is computer applications that simulate human intelligence or imagination to think, monitor, detect, and respond swiftly to threats of crime (David, Mustapha & Abubakar, 2025; Ikiyei & Amassomowei, 2025). The application of AI-enabled technologies in mitigating oil theft is sacrosanct. For instance, AI-powered drones and satellite imaging can be used to monitor oil pipelines and detect any anomaly or unauthorised activities in real time. By analysing the data collected through these technologies, authorities can identify hot spots of potential theft and take proactive measures. Bello, Odor, Busari, Ali, Girei, Alabi and Stephen (2025)noted thatAI offers a transformative solution to the challenges posed by oil theft. The authors observed that unlike traditional methods, AI-powered systems can provide real-time monitoring, predictive analysis, and automated threat detection. It also has the capacity to analyze oil flow in the pipeline and identify irregularities.

According to Ibrahim (2022), addressing the problems leading oil theft in the Niger Delta region requires high-breed technologies. Adelowo and Oladele (2022) noted that an anti-theft tracking system, such as pressure sensors can detect if pipelines and their contents were tampered. Similarly, it has been revealed that smart-pipeline technologies, such as fiber-optic sensors can detect leakages and compromise on oil pipelines. Adesuji (2026) argued that AI techniques such as machine learning, computer vision, neural networks, and predictive analytics can process vast amounts of data from sensors, drones, and supervisory control systems to detect anomalies, predict potential failures, and recommend optimal maintenance strategies. The author maintained that, given the growing complexity of Nigeria’s oil and gas operations, traditional manual and periodic inspection methods are no longer adequate. Therefore the adoption of AI enhanced technologies, such as drones, and remote sensing remains sacrosanct in dealing with oil theft.

AI Enhanced Technologies and Communities Mitigating Measures to Oil Theft

Wizor and Wali (2020) examined crude oil theft and oil companies-host communities’ conundrum in the Niger Delta. The study revealed that technological installations, such as satellite systems, CCTV and other digital instruments were some strategies adopted in monitoring activities of security men and criminals in the catchment areas. This suggests that not just that criminals are being monitored, but the compromise of State actors is not ruled out. This informs why activities of security personnel are being scrutinised closely using AI enabled devices. Similarly, Adelowo and Oladele (2022) examined the effectiveness of artificial intelligence and internet of things (IoT) in curbing oil theft in Nigeria. The study revealed that AI and the internet of things have been incorporated into Nigeria’s oil industry to tackle oil theft. It further posited that technologies, such as satellites, sensors, and communication devices are effective AI-powered devices in combating oil theft in the country.

Bello et al (2025) examined AI-assisted crude oil bunkering and illegal theft detection in the Nigerian oil and gas industry. The results showed there is application of AI in oil theft prevention and detection via machine learning, computer vision, and predictive analytics. The study submitted that AI has revolutionalised monitoring and control of oil and gas infrastructures in Nigeria. Another study by Adedoyin (2026) on artificial intelligence applications in pipeline monitoring and maintenance for sustainable oil and gas operations in Nigeria revealed that integration of AI-powered technologies has improved pipeline monitoring, maintenance, and overall system reliability.

Furthermore, Akaenye and Onosakponome (2023) examined youth restiveness and the challenges of oil theft in Niger Delta Region of Nigeria. The findings revealed that the government’s amnesty programme and engagement of ex-militants in pipelines surveillance helped to address restiveness. It also indicated that community engagement and access to the benefits from oil revenue mitigated oil theft in the area. Similarly, Gimah and Kobani (2024) made an assessment of efforts in discouraging crude oil theft in selected communities in Rivers State. The study found that community strategies for mitigating oil theft in the area included; entrepreneurship education, peace education, human rights advocacy, value-reorientation, agricultural extension and environmental education. However, the upsurge in oil theft in recent times clearly suggests that success has not been achieved.

Theoretical Orientation: Queer Ladder Theory

Queer ladder theory was developed by the American Sociologist, Daniel Bell (1919-2011) in an attempt to explain the instrumental essence of organized crime as a desperate means of economic empowerment and social climbing (Okoli & Agada, 2014). The theory’s basic assumption is that organized crime is an instrumental behavior and a means to an end. It is an instrument of social climbing and/or socio-economic advancement. It is also a means to accumulate wealth and build power (Okoli & Orinya, 2013).

The theory offers an in-depth understanding of the complex power dynamics, resistances, and community resilience towards oil theft in Warri Southwest. What this theory entails is that organized crime thrives in contexts where the government’s capacity to dictate, sanction, deter and control crime is poor; where public corruption is endemic; and where prospects for legitimate livelihood opportunities are slim (Okoli & Orinya, 2013). In such situations, the motivation to indulge in crime will be high, while deterrence from criminal living is low. In other words, the benefits of committing a crime surpass the costs and/or risks.

In understanding oil theft in Warri Southwest through the lens of this theory, it points to different angles. One angle is the oil theft activities that are being carried out by organized criminal groups and being facilitated by multi-national corporations and corrupt government officials, who use instrumentality or privileges of the State to indulge in illegal oil activities, such as smuggling of crude oil. On the other hand, the natives who are struggling to make ends meet sees the illegal sale of oil as a means of survival and also perpetrate this crime. It is also important to highlight that despite being rich in oil resources, poverty rate has remained high within the area. This is due to the systemic alienation or marginalization of the oil producing communities. Thus, from the lens of queer ladder theory, oil theft on the part of the community members could be seen as a response to marginalization and economic disparity, which creates an incentive or means of survival.

Materials and Methods

Mixed-methods research design was adopted. The choice of this design is that there is a combination of quantitative and qualitative methods. The general or universal population was one hundred and thirty-three thousand, three hundred and fifty-one (133,351) residents of Warri Southwest LGA of Delta State, while the target population was twenty-two thousand, two hundred and thirty-four (22,234) persons which comprised men group, women group, youth association and members of the traditional rulers council in the area. The sample size is 1,250 and it was statistically generated using Fisher, Laing, Stoeckel and Townsend (1998) formula. The choice of this category of persons is because they are adults and were informed on oil activities in their communities. In addition, in-depth interviews were conducted on notable individuals in the area, such as a Civil Defence Officer, a Vigilante leader, a Naval Officer, a DSS personnel, and a Civil Society Organization member. The choice of these personalities was based on the fact that they are actively involved in the security and advocacy for proper oil resources management. Instruments for data collection were structured questionnaire and in-depth interviews (IDI) guide. Multi-stage sampling techniques were used in selecting the respondents. Quantitative data were analysed using percentage, frequency, bar and pie charts, while qualitative data were thematically analysed through extraction and interpretation of quotes. Hypothesis was tested using multi-nominal logistic regression model.

Results and Discussion

This study administered 1,250 copies of questionnaire, out of which 922 copies that were properly filled were retrieved and used for analysis. This represent 74% response rate and was considered adequate for analysis. Findings are extensively discussed and related to the studies reviewed, thereby highlighting areas of convergence and divergence. The analyses are carried out in line with the specific objectives as follows;

Analysis of Objective One

The respondents’ level of awareness about AI enabled technologies in the control of oil theft in their communities was sought for. In doing this, the respondents were asked to indicate whether or not they were aware of theses technological devices. Their responses are presented in figure 1;

Fig 1. Respondents’ opinion on the awareness of AI technological tools used in controlling oil theft in their communities

Field Survey, 2025

The quantitative findings of figure 1 reveals that majority of the respondents (66.2%) had awareness of AI enabled technological tools being used to detect and control oil theft in their communities in Warri Southwest LGA. By contrast, 23.2% indicated they were not aware of such tools, while 10.6% expressed uncertainty. These results suggest that although awareness of AI enabled technological interventions is relatively widespread, there remains a significant minority who either lack knowledge or feel uncertain about the existence of such measures.

Furthermore, thematic evidence of the IDIs provides nuanced insights into the awareness gap. While the State security personnel interviewed demonstrated a more detailed familiarity with specific AI enabled technologies, the vigilante member reflected a more general or uncertain awareness. For example, an interviewee who is a Civil Defence Officer had this to say:

I am aware that presently the federal government has put some technology measures… just like when you are installing… CCTV cameras and using drones and all that, some of these measures have been used to send signal to security agents about the activities of oil thieves in this place (Civil Defense Officer, Female, 40, Kurutie, Warri Southwest LGA).

This aligns closely with the 66.2% who indicated awareness, but also shows that knowledge may be more conceptual than technical. Another interviewee reinforced this by referencing newer technological systems:

“There is now an emergence of hybrid AI technologies… CTV cameras are also being put in place to safeguard all these pipelines” (Naval Officer, Male, 36, Kurutie, Delta State).

Such statements reflect both awareness and confidence in specific AI monitoring tools, echoing the patterns captured quantitatively. By contrast, the community-based perspective conveyed more uncertainty, mirroring the 23.2% who reported no awareness and the 10.6% who were unsure. As one interviewee put it:

“If government see any technology that will help, it will be good… cameras can be mounted inside bush… or if they give us drones” (Vigilante Member, Male, 50, Oporoza, Warri Southwest LGA, Delta State).

This submission suggests openness to technological solutions but a lack of concrete knowledge about what systems are currently deployed. These data highlight that differences matter in terms of perspective and awareness about technologies adopted in detecting oil theft. The State actors demonstrated greater specificity, mentioning technologies such as CCTV and Combat Information Centers, while the other category of interviewees provided a more general perspective, highlighting uncertainty and some level of community awareness without technical detail. Generally, the findings indicate that there is a high level of awareness amongst residents of Warri Southwest on AI enabled technologies used in the control of oil theft in the area.

Analysis of Objective Two

The AI surveillance technologies used in the control of oil theft in Warri Southwest LGA are analysed hereunder. In doing this, the respondents were first asked to outline the AI surveillance technologies known to them in the control of oil theft in their communities and their responses are presented in figure 2;

Fig. 2. Respondents’ views on AI enabled communication technologies used to detect oil theft in their communities.

Field Survey, 2025

Figure 2 shows that respondents identified a wide variety of AI enabled technologies employed in detecting or preventing oil theft in their areas. Pipeline surveillance drones were the most frequently reported (18.3%), followed by community-based reporting strategies (14.8%), smart metering and flow monitoring systems (14.6%), and satellite imagery with remote sensing (14.5%). In addition, acoustic leak detection sensors (10.8%), fibre-optic pipeline monitoring systems (10.6%), and aerial patrols using helicopters or aircraft (10.5%) were other technological tools identified. However, while a smaller proportion of the respondents (3.9%) showed that they lacked knowledge about the technologies in use, 2.0% mentioned other tools not captured in the questionnaire.

Furthermore, the respondents were asked to indicate other AI-powered surveillance technologies they considered applicable in the control of oil theft in their areas but were not captured in the questionnaire, and their opinion are presented in table 1:

Table 1: Respondents’ opinion on AI surveillance technologies used in oil theft control in their communities

Response Options	Response Analysis
Response Options	Frequency	Percent
Drones (Unmanned Aerial Vehicles)	166	23.7%
CCTV cameras	140	21.4%
Satellite monitoring systems	173	15.4%
Motion sensors or ground-based detectors	120	10.7%
Security patrols using advanced monitoring equipment	169	15.1%
Not Sure	101	9.0%
Other	53	4.7%
Total	922	100.0%

Field Survey, 2025.

Table 1 identified several AI-powered surveillance technologies used in controlling oil theft in the study area. The most frequently selected AI enabled technologies were drones or unmanned aerial vehicles (23.7%) and CCTV cameras (21.4%). Satellite monitoring systems (15.4%) and security patrols equipped with advanced monitoring devices (15.1%) were also commonly reported. Motion sensors or ground-based detectors accounted for 10.7% response. In addition, 9.0% of the respondents indicated that they were not sure which AI enabled technologies were in use, while 4.7% cited other forms of surveillance devices. These results suggest that aerial and visual surveillance technologies, particularly drones and CCTV were the most widely recognized by respondents, while ground-based or less visible technologies appear to be less frequently reported. These findings suggest that oil theft detection and control efforts in Warri Southwest rely on a mix of advanced AI surveillance technologies and community-driven intelligence systems.

In addition to the quantitative results, IDIs evidence corroborates survey patterns. Interviewees in the security sector demonstrated familiarity with AI technologies. For instance, one interviewee expressed his opinion as follows;

“We have what is called CIC, Combat Information Center. It is a hybrid AI enabled technology.. Inside the CIC, you will be able to see far inside the ship. It has area location tracker…satellite…tracking the whole environment. There’s also what is called IFF on board bigger ships. IFF means International Friend or Foe. If it is a ship that come to take our oil, the Nigeria Navy ship that has that IFF will send signal of that IFF, eh, identification of friend or foe. Once that thing goes to the ship and there was no respond, then the Nigeria Navy ship will know that, uh, that vessel is not a friendly vessel. Thereby, now from there, they can send the gun boat or patrol boat, Nigeria Navy patrol boats to the area to go and arrest the vessel” (Naval Officer, Male, 36, Kurutie, Delta State).

This aligns with the survey’s mention of AI satellite imagery and remote sensing (14.5%). Similarly, the transcripts reinforced the importance of drones, the single most frequently reported tool in the survey (18.3%). As one interviewee noted thus;

“The use of CCTV and drones to secure oil pipeline instead of using human security have been useful. You know that the world has gone digital, um … Most oil-producing countries are using CCTVs, and drones to secure their oil pipelines. So the use of AI hybrid technology facilitates the emergence of optimal security, and protection in oil pipeline because you don’t need to be there to strike at the individuals who engage on this. You can track and strike from the control centre” (DSS Personnel, Male, 40, Okporoza, Warri Southwest LGA).

This submission reflects both the drone category and the 14.6% citing CCTV-style monitoring systems. To another interviewee, this is what he had to say;

“AI video recording cameras can be useful… or government should give us drones… we think if they (drones) hover around this area, the people disturbing us and stealing our oil will be afraid. You will not see them again” (Civil Defense Officer, Female, 40, Kurutie, Warri Southwest LGA).

This submission again validates the strong emphasis on aerial surveillance in the quantitative findings. Beyond AI high-tech tools, the thematic data also highlights community-based intelligence and measures, resonating with the 14.8% who identified community-reporting strategies. The Vigilante leader who is one of the interviewees said;

“If these AI technologies you are talking about are made available to us, they will need to teach us, train us on how to carry and use them. We are ready to use technology to deal with this problem” (Vigilante Member, Male, 50, Okporoza, Warri Southwest LGA).

This submission emphasizes the readiness and willingness of local actors in adopting AI technological systems to tackle the problem of oil theft in Warri Southwest. In summary, the quantitative and qualitative findings converge on the conclusion that the detection and control of oil theft in Warri Southwest relies on AI hybrid technologies, such as drones, satellites, and CCTV for real-time surveillance, alongside community-based mechanisms that provide local knowledge and rapid reporting. This triangulation underscores the role of both formal security infrastructures and grassroots involvement in addressing oil theft. These findings align with Wizor and Wali (2020) who reported that satellite systems, CCTV and other digital instruments were AI-aided devices used in monitoring oil theft in Niger Delta region.

Analysis of Objective Three

The effectiveness of AI enabled communication technologies in the control of oil theft in Warri Southwest LGA was examined. In doing this, the respondents were asked to make their assessment on the effectiveness level of the devices and their opinions are presented in figure 3;

Fig. 3. Respondents’ views on perceived effectiveness of AI enabled communication technologies in reducing oil theft.

Field Survey, 2025.

Figure 3 shows mixed views regarding the effectiveness of AI enabled communication technologies in the control of oil theft in Warri Southwest LGA. The result reveals that majority of the respondents (36.0%) perceived these technologies as ineffective, and 27.6% also considered them to be very ineffective. In contrast, only 15.6% considered them effective, while a smaller fraction (3.5%) reported that they are very effective. This suggests that less than one-fifth of the overall respondents considered the devices to be effective. However, 17.2% of the respondents expressed a neutral position, implying lack of knowledge. In summary, the findings suggests that negative perceptions outweighed positive assessments, implying that most respondents were skeptical of the effectiveness of AI enabled communication technologies in addressing oil theft in their communities.

The qualitative data provide nuance insight to the survey results. While the interviewees acknowledged the potential of technology, they expressed skepticism about its current effectiveness, aligning with the respondents who rated it ineffective. For example, one interviewee considered AI enabled technologies to be having great usefulness, but expressed some reservations in their limitations:

“The technologies are good, and very, very effective in addressing oil theft, but you can hardly catch these guys if you don’t have intelligence report” (DSS Personnel, Male, 40, Okporoza, Warri Southwest LGA).

This submission reflects the survey’s minority (15.6%) who gave positive evaluations, while also highlighting the conditional nature of their perceived effectiveness. Another interviewee who is a member of Civil Society Organization (CSO) expressed her dissatisfaction in the effectiveness of the available technologies as follows:

“We need more technologies…the ones available here are obsolete…if the federal government can add more, it would help” (Civil Society Member, Female, 38, Kurutie, Warri Southwest LGA).

This submission points to the ineffectiveness of the technologies due to acute shortage and poor institutional presence in oil-producing communities. Similarly, a vigilante leader emphasized the perceived inefficiency of the AI enabled technological tools due to neglect and breakdowns. He was quoted as saying;

“Technology is good, but they hardly bring new ones to this place. Even the available ones are not in good condition and some have spoilt long ago” (Vigilante Member, Male, 50, Okporonza, Warri Southwest LGA).

This underscores both logistical failures and the consequences of poor maintenance culture, leading to inefficiency to technological tools in detecting and controlling oil theft in the area. The challenge was further reinforced by State security actors who stressed inefficiency of AI technological tools due to the mismatch between their capacities and the sophistication of the tools. One of the interviewees recounted thus:

“We have been trying to use them…but no proper training, no support… how can we operate them without proper training on how to use them? I must say we are struggling to use the technologies due to lack of training and support” (Civil Defense Officer, Female, 40, Kurutie, Warri Southwest LGA).

These perspectives reinforce why respondents overwhelmingly perceive AI technologies as ineffective: even where systems exist, they are underfunded, unevenly deployed, or not accompanied by sufficient training for local actors. Taken together, the quantitative and qualitative evidence converge to show that while surveillance technologies are recognized and seen as potentially useful, their current implementation is inadequate, leaving communities skeptical of their actual impact. However, it is important to note that the strong majority perception of the ineffectiveness is not a rejection of the technologies, but a reflection of gaps in resourcing, deployment, and integration with community-based security efforts. This finding slightly corroborates that of Adelowo and Oladele (2022) who noted that AI-powered devices are effective in combating oil theft.

Test of Hypothesis

There is a significant positive relationship between respondents’ occupational group and their awareness of AI enabled technological tools for detecting oil theft in Warri Southwest LGA. In testing this hypothesis, the respondents’ occupational group and their awareness of AI enabled technological tools for detecting oil theft were cross-tabulated, and tested using multi-nominal logistic regression. The result is presented in table 2;

Table2: Model fit statistics for multinomial logistic regression predicting awareness of oil theft detection technologies by occupation

Statistic	Value	Df	P
Model χ² (Final vs. Intercept Only)	3.817	12	.987
-2 Log Likelihood (Final)	65.892	–	–
Cox & Snell R²	.004	–	–
Nagelkerke R²	.004	–	–
McFadden R²	.002	–	–

Field Survey, 2025

A multinomial logistic regression was conducted to assess whether occupational positions of the respondents predicted their awareness of oil theft detection using AI enabled technologies. The overall model was not statistically significant, χ²(12) = 3.817, p = .987, with a NagelkerkeR² of .004, indicating negligible explanatory power. No occupational category significantly predicted awareness of oil theft detection using AI enabled technologies when compared to the reference group (“Not Sure”). In other words, respondents’ awareness of AI technological tools for detecting oil theft did not differ in any meaningful way across occupational groups. This suggests that knowledge of such technologies is relatively uniform across employment categories, regardless of whether respondents were unemployed, farmers, artisans, civil servants, traders, or in other forms of work.

The hypothesized association between occupational group and awareness of AI enabled technologies used in detecting oil theft turned to be untrue. This implies that people within the selected communities in Warri Southwest had similar levels of awareness about the technologies employed in detecting oil theft within their communities, irrespective of their occupational roles. Therefore, this paper submits that there is no significant positive relationship between respondents’ occupational group and their awareness of AI enabled technological tools for detecting oil theft in Warri Southwest LGA.

Conclusion and Recommendations

It is acknowledgeable that the respondents align with the fact that AI-powered technologies are essential in curbing oil theft in their area. The accuracy in analysis, precision and swift response of AI in tracking, dictating and responding to oil thievery cannot be overemphasized. However, the lack of adequate awareness, and training on how to deploy or use these technologies were the major constraints. When adequately addressed and synergized with the host communities control strategies, the phenomenon of oil theft in the Warri Southwest in particular and the Niger Delta region in general will be drastically curtailed. Therefore, this paper concludes that the application of technologies is sacrosanct in curbing oil theft, especially when blended with indigenous knowledge and synergy. Therefore, the following recommendations are made for policy direction;

There is need for intensified awareness in the communities. It is not something that the State security operatives should be aware of at the detriment of the natives. Carrying the people along will help create synergy between residents and State actors in the fight against oil theft in Warri Southwest LGA..
There is need for security agencies to deploy round-the-clock AI thermal drone surveillance technologies, focusing on high-risk pipelines and creeks during peak operating hours. Real-time surveillance should be linked with community vigilante reporting systems for prompt detection and deterrence.
There should be creation of State-level Oil Security Technology Hubs to train local technicians for maintenance of surveillance systems, and integrate community reporting platforms into the national monitoring dashboards. This will enhance sustained functionality, efficiency, and promote local ownership of anti-theft AI enabled technologies.

References

Adedoyin, A. (2026). Artificial intelligence applications in pipeline monitoring and maintenance: A pathway to sustainable oil and gas operations in Nigeria. World Journal of Engineering Research & Technology, 12(2), 19–34. https://doi.org/10.5281/zenodo.18440992

Adegbite, I. (2013). Climate change, perennial crude oil theft and the quest for sustainable development in Nigeria. The Nation, 30^th March.

Adelowo, E., & Oladele, F. (2022). Application of artificial intelligence and internet of things. International Journal of Research and Scientific Innovation, 7(1), 22. https://www.rsisinternational.org

Adesuji, A. (2026). Artificial intelligence applications in pipeline monitoring and maintenance: A pathway to sustainable oil and gas operations in Nigeria. World Journal of Engineering Research & Technology, 12(2), 19–34. https://doi.org/10.5281/zenodo.18440992

Akpan, P. L., Ufomba, R. E., Ibewke, C. C. & Ufomba, A. J. (2017). Pipeline vandalisation and its implications on oil host communities of the Niger Delta (2003-2015). International Journal of Recent Research in Commerce, Economics & Management (IJRRCEM), 4(2), 42-52.

Al-Makhifi, J. (2013). Algeria smuggling crackdown cuts fuel line to Morocco. Arab News, 28 September. Available at: https://www.arabnews.com/news/466102 (accessed 22nd September 2025).

Asuni, J. B. (2009). Oil theft in Nigeria. The United States Institute of Peace Washington D.C.: Special Report No.: 229.

Bello, A, R., Odor, G, E, Busari, I, A., Ali, A, I., Girei, A.A., Alabi, J. O., & Stephen, V, I. (2025). AI-assisted crude oil bunkering and illegal theft detection in the oil and gas industry. IRE Journal, 8(10), ISSN: 2456-8880.

David, D., Mustapha, A & Abubakar, S. I. (2025). Role of Artificial Intelligence (AI) in Intelligence Gathering and Management of Nigeria National Security. International Journal of Sub-Saharan African Research (IJSSAR), 3(3), 206-225.

Enuoh, R. O., & Inyang, B. J. (2014). Effective management of corporate social responsibility (CSR) for desired outcome: The Niger Delta issue in Nigeria. International Journal of Business Administration, 5(4), 32-38.

Eric, A., & Oluwagbenga, H. M. (2017). Oil theft, illegal bunkering and pipeline vandalism: Its impact on Nigeria’s economy, 2015–2016. IIARD International Journal of Economics & Business Management, 3(2). https://www.iiardpub.org

Fisher, A. A., Laing, J. E., Stoeckel, J. E. & Townsend, J. W. (1998). Handbook for family planning operations research design. (2^nd ed.). New York: Library of Congress.

Ibaba, I. (2021). Oil theft, political corruption, and transnational criminal networks in the Niger Delta. Journal of African Studies, 18(2), 143–160.

Ibrahim, J. (2022, September 16). Oil theft and collapse of the Nigerian rentier state. Premium Times. Retrieved 22^nd March, 2025, from https://www.premiumtimesng.com/opinion/554367-oil-theft-and-collapse-of-the-nigerian-rentier-state-by-jibrin-ibrahim.html

Ikiyei, P. K. & Amassomowei, F. I. (2025). Artificial intelligence: An innovative technology towards solving insecurity challenges in Nigeria. European Journal of Contemporary Education & E-Learning, 3(3), 60-67. DOI: 10.59324/ejceel.2025.3(3).05

Jia, H. (2024). Combating oil theft, Thisday Newspaper online article.

Khazov-Cassia, S. (2021). The Great Russian oil heist: criminals, lawmen, and the quest for liquid loot’ Radio Free Europe/Radio Liberty (RFE/RL), 22^nd March. Available at: https://www.rferl.org/a/russia-oil-pipeline-theft-transneft/31163179.html (accessed 10th September 2025).

Mallo, S. (2024). The deployment of UAVs using machine learning for security surveillances on the pipelines and illegal oil refining clusters: A critical review of the Nigerian scenario. International Journal of Advanced Engineering & Management Research, 9(1). ISSN: 2456-3676, http://www.ijaemr.com 

NEITI (2023). Annual Report of the Nigerian Extractive Industry Transparency Initiative, NEITI, Abuja, Nigeria.

Obasi, N. K. (2011). Organized crime and illicit bunkering: Only Nigeria’s problem? In Michael Roll & Sebastian Sperling (Eds.), Fuelling the world – failing the region? Oil governance and development in Africa’s Gulf of Guinea, pp. 55-72, Abuja: Friedrich Ebert Stiftung.

Obi, C. (2022). The security dimensions of oil theft in Nigeria: Emerging trends and policy implications. African Security Review, 17(1), 98–115.

Odalonu, H. B. (2015). The upsurge of oil theft and illegal bunkering in the Niger Delta region of Nigeria: Is there a way out? Mediterranean Journal of Social Sciences, 6(3), 563-573. https://doi.org/10.5901/mjss.2015.v6n3s2p563

Okoli, A. C & Agada, F. T. (2014). Kidnapping and national security in Nigeria. Res Hum Soc Sci 4(6):137–146.

Okoli, A. C & Orinya, S. (2013). Oil pipeline vandalism and Nigeria’s national security. Global Journal of Human Social Sciences (F: Political Science), 13(10), 37-46.

Onuoha, F. C. (2008). Oil pipeline sabotage in Nigeria: Dimensions, actors and implications for national security, African Secur. Rev., 17(3), 99–115.

Onwuchekwe, S. I, Okafor, F. C. & Madu, E. T. (2020). A perception study of the Niger-Delta community on an ECOWAS role in the Niger Delta security crisis. Nigerian Journal of African Studies, 2(2), (ISSN: 2734

Onwuchekwe, S. I., Ezeah P. & Ikezue, E. C. (2025). Prevalence, Causes and Consequences of Oil Theft in the Niger Delta, Nigeria, Think India Journal, 28(4), 67-73.

Ralby, I. M. (2017). Downstream oil theft: implications and next steps. Washington, DC: Atlantic Council Global Energy Center. Available at: https://www.atlanticcouncil.org/wp-content/uploads/2017/03/Implications_and_Next_Steps_RW_0316.pdf (accessed 5th August 2024).

Ralby, I. M., Ralby, R. & Soud, D. (2017). Downstream oil theft: global modalities, trends, and remedies. Washington, DC: Atlantic Council Global Energy Center. Available at: https://www.atlanticcouncil.org/wpcontent/uploads/2017/01/Downstream_Oil_Theft_web_0327.pdf (accessed 16th September, 2021).

Romsom, E. (2022). Countering global oil theft: Responses and solutions. Available online: http://hdl.handle.net/10419/259391

Saje, P., & Abubakar, S. A. (2023). An assessment of the impact of oil theft on sustainable economic development in Nigeria. Journal of Business & Economics, 14(2), 80-86. Doi: https://doi.org/10.15341/jbe(2155-7950)/02.14.2023/004

Ume, I. S., & Chukwu, C.C (2019) Democracy And Sustainable Development In Nigeria: Challenges And Prospects In The Niger Delta, GSJ: Volume 7, Issue 9, September 2019 ISSN 2320-9186

Wizor, C. H. & Wali, E. (202). Crude oil theft in the Niger Delta: The oil companies and host communities conundrum. International Journal of Research & Scientific Innovation, 7(1), 22-32.

Yusuf, K. (2022, September 8). Analysis: How broke Nigeria lost $10 billion to crude oil theft in seven months. Premium Times Newspaper. https://www.premiumtimesng.com/news/headlines/552781-analysis-how-broke-nigeria-a’lost-10-billion-to-crude-oil-theft-in-seven-months.htm

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Content Validity Testing of Items for Determining the Appropriateness of a Computer Science-Specific Learning Taxonomy Instrument

Citation

Shaheed, I. M., Khudhair, K. T., & Hasan, N. F. (2026). Content Validity Testing of Items for Determining the Appropriateness of a Computer Science-Specific Learning Taxonomy Instrument. International Journal of Research, 13(4), 155–167. https://doi.org/10.26643/ijr/edupub/12

Iman Mousa Shaheed^1,*, Kifah Taha Khudhair², Noor Flayyih Hasan³

¹General Directorate of Education in Najaf, Kufa department of education, Najaf, Iraq

²Technical College of Management – Kufa, Al-Furat Al-Awsat Technical University, Kufa, 54003, Iraq

³Southern Technical University, Thi-Qar Technical College, Department of Accounting Techniques, Iraq

*Corresponding author: eman_musa21@yahoo.com

Abstract

Classification of learning objectives using taxonomies can substantially impact teaching and learning processes if the classifications are appropriate to the subject being taught. There is currently a trend toward the development of computer science-specific taxonomies. However, a tool for determining a new taxonomy’s appropriateness does not exist yet due to a lack of agreement regarding the appropriateness of a taxonomy. The purpose of this study was to determine the content validity of an instrument for assessing the appropriateness of a computer science-specific taxonomy. Five individuals specializing in computer science instruction judged the content validity of individual items on a four-point scale. These experts recommended minor revisions to improve the clarity or wording of the items, and these suggestions were incorporated into the instrument. The individual content validity index (I-CVI) and scale content validity index (S-CVI) were calculated. All the I-CVIs were 1.0, and the average scale content validity index was 1.0. The panel determined that all the items possessed sufficiently high content validity. This degree of content appropriateness indicates that the next stage of instrument development can occur.

Keywords: Learning Taxonomy; Appropriateness; Instrument Development; Content Validity Index.

1.0 Introduction

Learning taxonomies are useful planning tools for instructors, helping them to assess curriculum and related educational objectives. With respect to computer science, educators have widely used Bloom’s taxonomy and its revised versions [1, 2]. However, numerous other computer science-specific taxonomies have also been recommended [3-5] because of the original taxonomy’s unsuitability for learning computer science subjects [6]. Teodorescu, Bennhold [7] asserted that to help educators plan and assess their teaching, taxonomies must suit their goals and include subject-specific requirements.

According to Kropp, Stoker [8], a major problem is providing evidence of a taxonomy’s appropriateness including the development of a valid statistical methodology and models.

Unfortunately, there are few studies of the development of such models. Hauenstein [9] suggested five general rules of taxonomy evaluation: it should be applicable, inclusive, consist of categories that are independent from one another, reflect a consistent order, and use terms that are relevant to the subject area. Inclusivity prevents standards from being omitted, and mutual exclusivity prevents overlapping categories in a taxonomy.

The purpose of this study was to determine the content validity of an instrument to assess the appropriateness of a computer science-specific taxonomy. The results address the existing knowledge gap, and this instrument will provide computer science educators with a reliable, valid, and convenient tool for selecting the best taxonomy to use in their teaching practices.

2.0 TAXONOMY Appropriateness

When describing the appropriateness of a taxonomy, one must first determine the subject-specific specifications of the taxonomy. To our knowledge, no studies have discussed these specifications in relation to computer science.

To address this gap in the literature, the authors reviewed 40 studies of the application of Bloom’s taxonomy in computer programming courses. The aim was to answer the following key research questions: What are the deficiencies affecting currently used learning taxonomies with regard to computer-programming courses?

To answer this question, qualitative content analysis techniques were used to analyze statements about the computer programming-related shortcomings of Bloom’s taxonomy. These shortcomings were used to develop specifications for the appropriate computer science-specific learning taxonomy. Since the current adoption of Bloom’s taxonomy by ACM and IEEE Computer Society [10] to categorize the learning results of the basic programming course in the prospectus of the ACM/IEEE-CS, this search was limited to investigating the weaknesses of the original Bloom’s taxonomy and its revised versions. However, this analysis may also indicate other weaknesses in existing Bloom-based taxonomies.

The next sub-section describes the study performed to identify the specifications of a computer science-specific taxonomy and the dimensions required to evaluate the appropriateness of this learning taxonomy.

2.1 Specifications Identification

The literature review in this investigation involved the use of search terms that were derived from the research question, for example, “taxonomies of learning”, “Computer science education”, “computer programming”, and “Bloom’s taxonomy”. This data mining involved the use of four major electronic databases: the ACM Digital Library, ScienceDirect, Springer, and Google Scholar. The title, abstract, and keywords were reviewed in the search for published journal papers, conference proceedings, workshops and excerpts from the relevant literature.

A qualitative content analysis was conducted using the NVivo version 10 qualitative software database (QSR International Pty Ltd, Burlington, MA, USA) and was guided by the procedure of Edwards-Jones [11] to partially automate our analysis of the discussion sections in the reviewed articles.

In particular, one of the authors performed a constant comparison analysis [12] of both deductive and inductive coding approaches [13]. In the deductive phase, the aforementioned rules by Hauenstein [9] were considered. This step was performed by reading the entire set of data. Then, the author chunked the data into smaller meaningful parts. The author then labeled each chunk with a descriptive title or a “code”. NVivo was used to highlight segments of the text that included coding representing a specific weakness. Each new chunk of data was then compared with previous codes so that similar chunks were labeled with the same code. After all the data were coded, the codes were grouped by similarity, and a theme was identified and documented based on each grouping.

As a result, comprehensive computer science-specific taxonomy specifications are proposed, namely, consistency, inclusivity, hierarchical adequacy, representativeness, usability, coherence, mutual exclusivity, and dimensional adequacy. Table 1 presents these primary dimensions along with the approach used and their descriptions.

To ensure inter-rater reliability, the data were coded first. Themes and randomly selected sample statements related to these themes were then given to two reviewers who had taken a course in qualitative research methods. The reviewers were Ph.D. holders in education whose research interests included computer science education. The reviewers were asked to code the documents based on the themes. The agreement between the two experts’ reports measured 86%.

Table 1 Computer Science-Specific Taxonomy Specifications

No	Dimension	Approach	Description
1	Usability	Inductive	The taxonomy should categorize programming learning objectives in a simple way that could break these objectives into their components (i.e. task(s) and knowledge(s)).
2	Consistency	Deductive	The taxonomy should involve a dependable classification and interpretation of programming learning outcomes. These outcomes should always be expressed the same way.
3	Learnability	Inductive	Taxonomic categories and their interpretations should be comprehensible.
4	Hierarchical adequacy	Deductive	The hierarchy of categories should effectively describe programming learning objectives.
5	Dimensional adequacy	Inductive	The taxonomy should have two distinct dimensions (knowledge types and cognitive processes) to successfully describe the constructive learning objectives of programming. According to Airasian and Miranda [14], a two-dimensional approach allows educators to create stronger objectives that address increasingly complex instruction methods.
6	Mutual exclusivity	Inductive	Each learning objective should be assigned to only one category.
7	Inclusivity	Deductive	The taxonomy should include a sufficient list of all necessary programming knowledge types and skills for the user to classify all programming learning standards.
8	Representativeness	Deductive	The taxonomy should use common relevant terms to describe programming skills, knowledge types, and competencies required for each skill. The programming knowledge framework should be considered [15, 16].

3.0 Instrument development

The development process of Lynn [17] was used to guide the content development for this instrument. In this process, when content is being developed for an affective measure such as one of taxonomic appropriateness, two sub-processes occur: development and judgment. Development involves the identification of dimensions or sub-dimensions and extends to item generation and the subsequent integration of items into a suitable form, according to Lynn [17]. Judgment involves determining whether the given content and instrument are sufficiently valid [17]. According to Turner, Quittner [18], during initial instrument development, a conceptual framework should be identified. This framework should be representative so that the domain content is specific and relates to the subject area. This specificity is achieved by reviewing the related literature, during which potential items are identified. Once the preliminary scope of the taxonomy has been identified, the proposed content is analyzed to achieve a satisfactory final structure.

3.1 Conceptual Framework and Domain Content Identification

Insufficient information exists on theories of measuring taxonomy appropriateness, and no substantive literature regarding the application of theoretical validity frameworks are yet available. However, we recommended that the framework presented in Table 1 be considered when developing a learning taxonomy for computer programming purposes. In addition, the taxonomy framework presented in Table 1 should include items that are representative of the domain of computer programming and that adequately support the validity of the construction. This representativeness is achieved by using the framework to guide the selection of specific content deemed suitable for fully developing the instrument.

3.2 Identification of Items

The identification of items involved writing items for the scales. Initially, items from a previously validated questionnaire, specifically, the Measurement Scales for Perceived Usefulness and Perceived Ease of Use, by Davis [19], was examined and adapted. Then, suitable items were written for each scale based on a review of the literature [3, 20-37], and these items were incorporated into the taxonomy framework and were finally related to particular dimensions. Table 2 shows the items developed for each dimension.

Table 2 Taxonomy Appropriateness Items.
Dimension	Items
1. Usability	1.1 This taxonomy is easy to use.
	1.2 This taxonomy is flexible in describing learning objectives.
	1.3 Using this taxonomy is effortless.
	1.4 This taxonomy gives me more control over the activities in my course.
2. Consistency	2.1 This taxonomy can be used to interpret programming learning tasks every time.
	2.2 This taxonomy can be used to interpret programming learning knowledge every time.
	2.3 This taxonomy can be used to classify programming learning outcomes every time.
3. Learnability	3.1 The categories in this taxonomy are comprehensible.
	3.2 The categories in this taxonomy can be clearly interpreted.
	3.3 This taxonomy is readable.
4. Hierarchical adequacy	4.1 The ordering of the taxonomy’s skill sets appropriately reflects the programming learning process.
	4.2 The ordering of the taxonomy’s knowledge types appropriately reflects the programming learning process.
	4.3 The ordering of the taxonomy’s categories appropriately reflects programming learning objectives.
5. Dimensional adequacy	5.1 This taxonomy includes enough distinctive dimensions of knowledge that can be used to successfully describe constructive programming learning objectives.
	5.2 This taxonomy includes enough distinctive dimensions of cognitive that can be used to successfully describe constructive programming learning objectives.
	5.3 This taxonomy includes enough distinctive categories that can be used to successfully describe constructive programming learning objectives.
6. Mutual exclusivity	6.1 When using this taxonomy, each knowledge type required in programming learning can be assigned to a single category.
	6.2 When using this taxonomy, each programming learning skill can be assigned to a single category.
	6.3 When using this taxonomy, each programming learning objective can be assigned to a single category.
7. Inclusivity	7.1 The set of knowledge types in this taxonomy include all necessary knowledge types that students must know to perform a given programming learning task.
	7.2 The skills in this taxonomy include all the necessary skills that students must acquire to perform a given programming learning task.
	7.3 The knowledge types in this taxonomy include all appropriate types that students must know to perform a given programming learning task.
	7.4 The skills in this taxonomy include all appropriate skills that students must acquire to perform a given programming learning task.
8. Representativeness	8.1 The categories in this taxonomy are relevant to learning computer programming.
	8.2 The knowledge types in this taxonomy are relevant to knowledge required to perform computer programming learning tasks.
	8.3 The skill sets in this taxonomy are relevant to skills that must be acquired by students to perform computer programming learning tasks.

4.0 Measuring Content Validity

Once the items have been generated, the validity of an item and of the overall instrument must be quantitatively determined [17]. In doing this, researchers frequently calculate a content validity index (CVI). Hambleton, Swaminathan [38] first presented this index and advocated its use in nursing research conducted by Waltz and Bausell [39].

Many factors guided the selection of this index, including its ease of calculation and understanding. In contrast, the content validity ratio (CVR) developed by Lawshe [40], for example, is easy to calculate but not as easy to interpret [41]. Another desirable quality of a content validity measure is that it yields item-level information that can be used to refine or discard items and a summary of the content validity of the overall scale [41].

The CVI is the percentage of respondents who assign an item a score of 3 or 4 on a 1–4 scale of relevance or representativeness. It has been recommended that an individual CVI (I-CVI) and a scale CVI (S-CVI) should be calculated separately and that the S-CVI be reported [39, 41-43].

Polit and Beck [42] preferred the S-CVI in cases where more content-expert panel members are involved because one hundred percent agreement is not feasible. The S-CVI is determined by averaging I-CVI scores. When six or more experts are involved, Lynn [17] recommended a minimum I-CVI of 0.78. However, Waltz and Bausell [39] recommended a minimum S-CVI value of 0.90 for a valid scale in which items should be retained. In this study, we use both the I-CVI and S-CVI to determine the content validity of statements related to taxonomy appropriateness.

4.1 Expert Panel

Lynn [17] argued that at least three experts should be consulted when performing content validation. Our expert panel included five subject matter experts with more than 10 years of teaching programming experience. These experts were invited to evaluate the content validity based on the I-CVI and S-CVI. Each respondent received an informational email that included a hyperlink to a questionnaire. Survey security was maintained using Secure Sockets Layer technologies to protect confidentiality, and no personal identifiers were collected. A four-point scale was used to evaluate the content validity, and the values were matched with verbal descriptions of taxonomic appropriateness as follows: 1 = the item is not representative; 2 = the item requires major revisions to be representative; 3 = the item requires minor revisions to be representative; 4 = the item is representative. The CVI was calculated as the percentage of experts who selected 3 or 4 when scoring the items. As prescribed in the proposed methodology of our study, both the I-CVI and S-CVI were calculated. The average scale CVI (S-CVI/Ave) was determined from all the I-CVI values. The target SCVI/Ave value, according to Polit and Beck [42], is 0.9.

For greater reliability, we then calculated a modified kappa statistic (k*) described by Polit, Beck [41]. According to Wynd, Schmidt [44], the kappa statistic is an important supplement to the CVI because it indicates the degree of agreement beyond chance. To assess the degree of agreement based on the value of κ*, the guidelines by Landis and Koch [45] are used.

5.0 Results and Discussion

Five experts (1 female) agreed to participate in the study. The expert panel consisted of two domain experts, each with more than six years of programming teaching experience. Additionally, three members of the panel had more than 11 years of experience, and two had more than 15 years of experience. The experts currently teach programming and are familiar with the classification of learning objectives in accordance with Bloom’s taxonomy. The panel was given an opportunity to provide feedback on whether they identified mistakes or ambiguities in any part of the instrument. They were also encouraged to suggest ways to improve the instrument.

According to Polit and Beck [42], the I-CVI of a new instrument should range between 0.78 and 0.80. As indicated, all the I-CVI scores for this instrument were 1.0. Therefore, all the items were retained in the questionnaire. Following the recommendations of Lynn [17], testing of a psychometric instrument should be conducted next. The expert panel assigned the instrument I-CVI scores of 1.0 (Table 3). Thus, the S-CVI/Ave value was recorded as 1.0, confirming that each individual item can be retained. The 26 items received an I-CVI value of 1.0. Because the CVI scores were consistently high, we concluded that none of the experts’ suggestions regarding item content needed to be adopted. The high degree of concurrence regarding taxonomy appropriateness among the respondents indicates that the instrument for assessing taxonomy appropriateness is adequate for progression to the next step of instrument development.

A modified kappa statistic (k*) was calculated to determine if there was agreement between the raters’ judgments regarding whether the 26 items regarding taxonomy appropriateness were relevant. There was high agreement between the five raters’ judgments of all the items: κ* = 1.0.

Table 3 Content validity indices (I-CVI and S-CVI)

Items	I-CVI	S-CVI/ave	k*	No. of Respondents
1. Usability
1.1 This taxonomy is easy to use.	1.0		1.0	5
1.2 This taxonomy is flexible in describing learning objectives.	1.0		1.0	5
1.3 Using this taxonomy is effortless.	1.0		1.0	5
1.4 This taxonomy gives me more control over the activities in my course.	1.0		1.0	5
2. Consistency
2.1 This taxonomy can be used to interpret programming learning tasks every time.	1.0		1.0	5
2.2 This taxonomy can be used to interpret programming learning knowledge every time.	1.0		1.0	5
2.3 This taxonomy can be used to classify programming learning outcomes every time.	1.0		1.0	5
3. Learnability
3.1 The categories in this taxonomy are comprehensible.	1.0		1.0	5
3.2 The categories in this taxonomy can be clearly interpreted.	1.0		1.0	5
3.3 This taxonomy is readable.	1.0		1.0	5
4. Hierarchical adequacy
4.1 The ordering of the taxonomy’s skill sets appropriately reflects the programming learning process.	1.0		1.0	5
4.2 The ordering of the taxonomy’s knowledge types appropriately reflects the programming learning process.	1.0		1.0	5
4.3 The ordering of the taxonomy’s categories appropriately reflects programming learning objectives.	1.0		1.0	5
5. Dimensional adequacy
5.1 This taxonomy includes enough distinctive dimensions of knowledge that can be used to successfully describe constructive programming learning objectives.	1.0		1.0	5
5.2 This taxonomy includes enough distinctive dimensions of cognitive that can be used to successfully describe constructive programming learning objectives.	1.0		1.0	5
5.3 This taxonomy includes enough distinctive categories that can be used to successfully describe constructive programming learning objectives.	1.0		1.0	5
6. Mutual exclusivity
6.1 When using this taxonomy, each knowledge type required in programming learning can be assigned to a single category.	1.0		1.0	5
6.2 When using this taxonomy, each programming learning skill can be assigned to a single category.	1.0		1.0	5
6.3 When using this taxonomy, each programming learning objective can be assigned to a single category.	1.0		1.0	5
7. Inclusivity
7.1 The set of knowledge types in this taxonomy include all necessary knowledge types that students must know to perform a given programming learning task.	1.0		1.0	5
7.2 The skills in this taxonomy include all the necessary skills that students must acquire to perform a given programming learning task.	1.0		1.0	5
7.3 The knowledge types in this taxonomy include all appropriate types that students must know to perform a given programming learning task.	1.0		1.0	5
7.4 The skills in this taxonomy include all appropriate skills that students must acquire to perform a given programming learning task.	1.0		1.0	5
8. Representativeness
8.1 The categories in this taxonomy are relevant to learning computer programming.	1.0		1.0	5
8.2 The knowledge types in this taxonomy are relevant to knowledge required to perform computer programming learning tasks.	1.0		1.0	5
8.3 The skill sets in this taxonomy are relevant to skills that must be acquired by students to perform computer programming learning tasks.	1.0		1.0	5
Scale		1.0
I-CVI, individual content validity Index; S-CVI/ave, average scale content validity index; k*, modified kappa statistic

6.0 Conclusions

The responses of the expert panel of programming instructors indicate that the proposed content presents a high degree of taxonomic appropriateness. We also found consistency in terms of agreement among the respondents, indicating that progression to the next phase of instrument development can commence. The 26 items that were considered in the taxonomy appropriateness questionnaire will be psychometrically tested through a pilot evaluation using item response theory. This step will determine whether construct validity in the context of computer programming as demonstrated by the questionnaire serves as an indication of taxonomy appropriateness. The outcomes of this next stage may influence the future selection of taxonomies that are appropriate for this subject area.

Acknowledgement

The authors are thankful to anonymous reviewers whose comments significantly improved this manuscript.

References

[1] Anderson, L., D. Krathwohl, and B. Bloom, A taxonomy for learning, teaching and assessing: a revision of Bloom’s taxonomy of educational objectives. 2001: Longman.

[2] Bloom, B.S., et al., Taxonomy of educational objectives: Handbook I: Cognitive domain. New York: David McKay, 1956. 19: p. 56.

[3] Fuller, U., et al., Developing a computer science-specific learning taxonomy. ACM SIGCSE Bulletin, 2007. 39(4): p. 152-170.

[4] Meerbaum-Salant, O., M. Armoni, and M. Ben-Ari, Learning computer science concepts with scratch. Computer Science Education, 2013. 23(3): p. 239-264.

[5] Santos, A., A. Gomes, and A. Mendes. A taxonomy of exercises to support individual learning paths in initial programming learning. in Frontiers in Education Conference, 2013 IEEE. 2013.

[6] Johnson, C.G. and U. Fuller. Is Bloom’s taxonomy appropriate for computer science? in Proceedings of the 6th Baltic Sea conference on Computing education research: Koli Calling 2006. 2006. ACM.

[7] Teodorescu, R.E., et al., New approach to analyzing physics problems: A Taxonomy of Introductory Physics Problems. Physical Review Special Topics-Physics Education Research, 2013. 9(1): p. 010103.

[8] Kropp, R.P., H.W. Stoker, and W.L. Bashaw, The Validation of the Taxonomy of Educational Objectives. The Journal of Experimental Education, 1966. 34(3): p. 69-76.

[9] Hauenstein, A.D., A conceptual framework for educational objectives: A holistic approach to traditional taxonomies. Vol. 100. 1998: University Press of America Lanham, MD.

[10] ACM and IEEE Computer Society. Computer Science Curriculum 2013. 2013; Available from: http://www.sigart.org/CS2013-EAAI2011panel-RequestForFeedback.pdf.

[11] Edwards-Jones, A., Qualitative data analysis with NVIVO. Journal of Education for Teaching, 2014. 40(2): p. 193-195.

[12] Leech, N.L. and A.J. Onwuegbuzie, An array of qualitative data analysis tools: A call for data analysis triangulation. School psychology quarterly, 2007. 22(4): p. 557.

[13] Hsieh, H.-F. and S.E. Shannon, Three approaches to qualitative content analysis. Qualitative health research, 2005. 15(9): p. 1277-1288.

[14] Airasian, P.W. and H. Miranda, The role of assessment in the revised taxonomy. Theory into practice, 2002. 41(4): p. 249-254.

[15] Mayer, R.E., From Novice to expert, in Handbook of Human-Computer Interaction (Second Edition), M.G. Helander, T.K. Landauer, and P.V. Prabhu, Editors. 1997, North-Holland: Amsterdam. p. 781-795.

[16] Shneiderman, B. and R.E. Mayer, Syntactic/semantic interactions in programmer behavior: A model and experimental results. International Journal of Computer & Information Sciences, 1979. 8(3): p. 219-238.

[17] Lynn, M.R., Determination and quantification of content validity. Nursing Research, 1986. 35(6): p. 382-385.

[18] Turner, R.R., et al., Patient-Reported Outcomes: Instrument Development and Selection Issues. Value in Health, 2007. 10: p. S86-S93.

[19] Davis, F.D., Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 1989: p. 319-340.

[20] Alaoutinen, S., Evaluating the effect of learning style and student background on self-assessment accuracy. Computer Science Education, 2012. 22(2): p. 175-198.

[21] Ming-Han, L., R. Guido, and ling, Integrating categories of algorithm learning objective into algorithm visualization design: a proposal, in Proceedings of the fifteenth annual conference on Innovation and technology in computer science education. 2010, ACM: Bilkent, Ankara, Turkey.

[22] Gluga, R., et al. Coming to terms with Bloom: an online tutorial for teachers of programming fundamentals. in Proceedings of the Fourteenth Australasian Computing Education Conference-Volume 123. 2012. Australian Computer Society, Inc.

[23] Athanassiou, N., J.M. McNett, and C. Harvey, Critical thinking in the management classroom: Bloom’s taxonomy as a learning tool. Journal of Management Education, 2003. 27(5): p. 533-555.

[24] Ari, A., Finding Acceptance of Bloom’s Revised Cognitive Taxonomy on the International Stage and in Turkey. Educational Sciences: Theory and Practice, 2011. 11(2): p. 767-772.

[25] Thompson, E., et al. Bloom’s taxonomy for CS assessment. in Proceedings of the tenth conference on Australasian computing education-Volume 78. 2008. Australian Computer Society, Inc.

[26] Amer, A., Reflections on Bloom’s revised taxonomy. Electronic Journal of Research in Educational Psychology, 2006. 4(1): p. 213-230.

[27] Shuhidan, S., M. Hamilton, and D. D’Souza. A taxonomic study of novice programming summative assessment. in Proceedings of the Eleventh Australasian Conference on Computing Education-Volume 95. 2009. Australian Computer Society, Inc.

[28] Johnson, G., et al., Applying the revised Bloom’s taxonomy of the cognitive domain to linux system administration assessments. J. Comput. Sci. Coll., 2012. 28(2): p. 238-247.

[29] Petersen, A., M. Craig, and D. Zingaro, Reviewing CS1 exam question content, in Proceedings of the 42nd ACM technical symposium on Computer science education. 2011, ACM: Dallas, TX, USA. p. 631-636.

[30] Kyllonen, P.C. and V.J. Shute, Taxonomy of learning skills. 1988, DTIC Document.

[31] Wong, G. and H. Cheung, Outcome-Based Teaching and Learning in Computer Science Education at Sub-degree Level. International Journal of Information and Education Technology, 2011. 1(1).

[32] Starr, C.W., B. Manaris, and R.H. Stalvey. Bloom’s taxonomy revisited: specifying assessable learning objectives in computer science. in ACM SIGCSE Bulletin. 2008. ACM.

[33] Gluga, R., et al., Mastering cognitive development theory in computer science education. Computer Science Education, 2013. 23(1): p. 24-57.

[34] Annett, J. and K.D. Duncan, Task analysis and training design. 1967, HULL UNIV. (ENGLAND), DEPT. OF PSYCHOLOGY.

[35] Johnson, G., et al. Multi-perspective survey of the relevance of the revised bloom’s taxonomy to an introduction to linux course. in Proceedings of the 13th annual conference on Information technology education. 2012. ACM.

[36] Bümen, N.T., Effects of the original versus revised Bloom’s taxonomy on lesson planning skills: a Turkish study among pre-service teachers. International Review of Education, 2007. 53(4): p. 439-455.

[37] Lahtinen, E. A categorization of Novice Programmers: a cluster analysis study. in Proceedings of the 19th annual Workshop of the Psychology of Programming Interest Group, Joensuu, Finnland. 2007.

[38] Hambleton, R.K., et al., Criterion-referenced testing and measurement: A review of technical issues and developments. Review of Educational Research, 1978: p. 1-47.

[39] Waltz, C.F. and B.R. Bausell, Nursing research: design statistics and computer analysis. 1981: Davis FA.

[40] Lawshe, C.H., A quantitative approach to content validity. Personnel psychology, 1975. 28(4): p. 563-575.

[41] Polit, D.F., C.T. Beck, and S.V. Owen, Is the CVI an acceptable indicator of content validity? Appraisal and recommendations. Research in nursing & health, 2007. 30(4): p. 459-467.

[42] Polit, D.F. and C.T. Beck, The content validity index: are you sure you know what’s being reported? Critique and recommendations. Research in nursing & health, 2006. 29(5): p. 489-497.

[43] Grant, J.S. and L.L. Davis, Selection and use of content experts for instrument development. Research in nursing & health, 1997. 20(3): p. 269-274.

[44] Wynd, C.A., B. Schmidt, and M.A. Schaefer, Two quantitative approaches for estimating content validity. Western Journal of Nursing Research, 2003. 25(5): p. 508-518.

[45] Landis, J.R. and G.G. Koch, The measurement of observer agreement for categorical data. biometrics, 1977: p. 159-174.

Abstract

2. Conceptual Framework

Concept of Geriatric Inmate

Concept of Community Re-entry

Therefore, this paper recommends that:

1. The Nigerian Correctional Service should establish transitional housing schemes and geriatric-specific healthcare access programs for elderly ex-inmates to address the pressing issues of homelessness and medical neglect post-release.

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