Critical factors for implementing effective information governance in Nigerian universities: A case study investigation

Muhammad, Jika Saidu; Isa, Azman Mat; Samsudin, Ahmad Zam Hariro; Miah, Shah Jahan

doi:10.1007/s10639-020-10206-3

Cited by 6 publications

(5 citation statements)

References 22 publications

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“…The process deployed the use of big data analytics over a range of AI techniques to achieve predictive accuracy, moderating classifier algorithm parameters, tuning the dataset, and applying ML algorithms to choose the best key predictive attributes. The application of DSR for predictive artefact design justifies technology-based innovations in non-information system disciplines such as education (Muhammad et al, 2020;Shah & Michael, 2016). For accuracy purposes, two design and development phases were employed.…”

Section: Sample Data and Populationmentioning

confidence: 99%

Towards Sustainable Education: A Machine Learning Model for Early Student Dropout Prediction in Higher Education Institutions

MUKOOYO,

PAUL KASSE

2024

UHERJ

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Sustaining learners through an education cycle is a challenge for institutions at all levels. For higher education institutions, learners are presumed to be mature enough to complete their study courses. However, the challenge of student dropouts is prevalent. This paper seeks to address the key question of why students continue to drop out of learning institutions despite interventions undertaken by stakeholders. The attrition rates are a major concern that requires immediate attention if sustainable education is to be achieved. Dropping out of school is attributed to both individual factors and external factors. However, both require mitigation to save the future of education. This paper presents an analysis of challenges leading to student dropouts sampled from five institutions within the central region of Uganda (532 respondents). In addition, we leveraged the power of artificial intelligence (AI) to design and present a machine learning model for early student dropout prediction so that early interventions can be undertaken. The study adopted the design science methodology to scientifically support the design and validation of the machine learning student dropout prediction model. The early warning model presents key performance indicators to signal whether a student is predisposed to drop out or on course to completion. This way corrective intervention can be undertaken early enough for likely dropout. The validation experiment was conducted on a sample of 523 from the five institutions predicated a dropout of 10%. This proved the concept and the capacity of the model to predict learner dropout from university.

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Section: Sample Data and Populationmentioning

confidence: 99%

Towards Sustainable Education: A Machine Learning Model for Early Student Dropout Prediction in Higher Education Institutions

MUKOOYO,

PAUL KASSE

2024

UHERJ

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“…As a result, CSFs are vital to an organization's success. These might be made for a certain department within the company or the entire organization (Muhammad et al, 2020).…”

Section: Critical Success Factorsmentioning

confidence: 99%

“…According to Correani et al (2020), the rapid rise of digital technologies and the massive amounts of data acquired each day by devices and applications are forcing organizations to change the corporate architecture they build and value │ 65 drastically. Learning institutions and universities must recognize the importance of assembling a team to engage with CSFs (Muhammad et al, 2020). Faculty members must offer suggestions or provide comments for the organization to improve.…”

Section: Critical Success Factorsmentioning

confidence: 99%

“…According to Muhammad (2020), workforce recruiting is a manifestation that creates an environment that allows records and information management professionals to carry out their duties effectively. Similarly, it shows that as their knowledge, abilities, and attitudes toward working, living, and learning in the knowledge society improve, they can produce publishable research results.…”

Section: Highly Manifestedmentioning

confidence: 99%

“…According to Ogar and Dushu (2018), innovation technologies have caused a paradigm change in libraries and information services. It is critical to have sufficient recordkeeping and information management (Muhammad et al, 2020). The use of information and communication technology under a flexible learning approach to optimize organizational innovations on the work of education professionals in the current context of a pandemic contributes to managing emotions and motivational processes, contributing to meaningful learning in students.…”

Section: Highly Manifestedmentioning

confidence: 99%

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Critical Success Factors of the Flexible Learning Delivery as Organizational Innovation of One State University in the Philippines

Aliazas

Panoy

Rosario

et al. 2021

IJEMDS

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The COVID-19 pandemic has caused the whole educational system to collapse during the lockdown period. Hence, higher education institutions (HEI) led the change of learning delivery mode phases for effective and efficient academic services. This study describes the extent of manifestation on critical success factors (CSF) observed in one state university and determines the degree of organizational innovation practices in terms of technologyenhanced instructions. It also investigates the dimensions of CSFs that singly or in combination predict organizational innovations. The study purposively chose 251 faculty members from Laguna State Polytechnic University-San Pablo City, Philippines as respondents. Multiple regression analysis was used to determine significant predictors of Organizational Innovations. The findings reveal that five (5) constructs of CSFs were highly manifested (IUQDDI, ILT, IFCE, ISC & ITQ) among respondents and organizational innovations concerning technology-enhanced instructions viewed as highly practiced. Likewise, moderate to a strong association was seen between CSFs and organizational innovations. Furthermore, ISC, ILT, IUQDDI, and IFCE as the CSF construct were deemed significant predictors of organizational innovations. The results served as basis for organizations' future planning on the effective implementation of the learning delivery mode.

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The effect of flipped learning on EFL students’ writing performance, autonomy, and motivation

Challob

2021

Educ Inf Technol

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Critical factors for implementing effective information governance in Nigerian universities: A case study investigation

Cited by 6 publications

References 22 publications

Towards Sustainable Education: A Machine Learning Model for Early Student Dropout Prediction in Higher Education Institutions

Towards Sustainable Education: A Machine Learning Model for Early Student Dropout Prediction in Higher Education Institutions

Critical Success Factors of the Flexible Learning Delivery as Organizational Innovation of One State University in the Philippines

The effect of flipped learning on EFL students’ writing performance, autonomy, and motivation

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