Design science research for decision support systems development: recent publication trends in the premier IS journals

Miah, Shah Jahan; McGrath, G. Michael; Kerr, Donald

doi:10.3127/ajis.v20i0.1482

Cited by 5 publications

(1 citation statement)

References 41 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%