A machine learning algorithm framework for predicting students performance: A case study of baccalaureate students in Morocco

Qazdar, Aimad; Er-Raha, Brahim; Cherkaoui, Chihab; Mammass, Driss

doi:10.1007/s10639-019-09946-8

Cited by 48 publications

(16 citation statements)

References 5 publications

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“…Coleman et al., 2019), (2) academic and social data , i.e., statistical analysis of course grades, standardised tests, disciplinary data, attendance rates (e.g. Qazdar et al., 2019) or (3) academic, social and demographic data , i.e., including gendered/racialised identity markers, age and socioeconomic indicators like free school meals (e.g. Sorensen, 2019).…”

Section: Predictive Analytics In Educationmentioning

confidence: 99%

Dashboard stories: How narratives told by predictive analytics reconfigure roles, risk and sociality in education

Jarke

Macgilchrist

2021

Big Data & Society

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In this paper, we explore how the development and affordances of predictive analytics may impact how teachers and other educational actors think about and teach students and, more broadly, how society understands education. Our particular focus is on the data dashboards of learning support systems which are based on Machine Learning (ML). While previous research has focused on how these systems produce credible knowledge, we explore here how they also produce compelling, persuasive and convincing narratives. Our main argument is that particular kinds of stories are written by predictive analytics and written into their data dashboards. Based on a case study of a leading predictive analytics system, we explore how data dashboards imply causality between the ‘facts’ they are visualising. To do so, we analyse the stories they tell according to their spatial and temporal dimensions, characters and events, sequentiality as well as tellability. In the stories we identify, teachers are managers, students are at greater or lesser risk, and students’ sociality is reduced to machine-readable interactions. Overall, only data marked as individual behaviours becomes relevant to the system, rendering structural inequalities invisible. Reflecting on the implications of these systems, we suggest ways in which the uptake of these systems can interrupt such stories and reshape them in other directions.

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Section: Predictive Analytics In Educationmentioning

confidence: 99%

Dashboard stories: How narratives told by predictive analytics reconfigure roles, risk and sociality in education

Jarke

Macgilchrist

2021

Big Data & Society

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show abstract

“…Supervised learning are either classification or regression algorithms. The classification algorithms comprises of input, output and the aim is to apply an algorithm to identify the mapping function from the input to the output (Qazdar et al, 2019). Each instance consists of independent variables (prediction features) and a dependent variable (prediction class).…”

Section: Literature Reviewmentioning

confidence: 99%

An artificial intelligence approach to monitor student performance and devise preventive measures

Khan

Ahmad

Jabeur

et al. 2021

Smart Learn. Environ.

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A major problem an instructor experiences is the systematic monitoring of students’ academic progress in a course. The moment the students, with unsatisfactory academic progress, are identified the instructor can take measures to offer additional support to the struggling students. The fact is that the modern-day educational institutes tend to collect enormous amount of data concerning their students from various sources, however, the institutes are craving novel procedures to utilize the data to magnify their prestige and improve the education quality. This research evaluates the effectiveness of machine learning algorithms to monitor students’ academic progress and informs the instructor about the students at the risk of ending up with unsatisfactory result in a course. In addition, the prediction model is transformed into a clear shape to make it easy for the instructor to prepare the necessary precautionary procedures. We developed a set of prediction models with distinct machine learning algorithms. Decision tree triumph over other models and thus is further transformed into easily explicable format. The final output of the research turns into a set of supportive measures to carefully monitor students’ performance from the very start of the course and a set of preventive measures to offer additional attention to the struggling students.

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“…Many studies have been carried out to predict the academic performance of students and their achievements of their major, as indicated in ref. [ 17 ]. The effects of this framework were tested in two phases.…”

Section: Related Workmentioning

confidence: 99%

Integrating machine learning and blockchain to develop a system to veto the forgeries and provide efficient results in education sector

Shah¹,

Patel²,

Adesara³

et al. 2021

Vis. Comput. Ind. Biomed. Art

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Although the education sector is improving more quickly than ever with the help of advancing technologies, there are still many areas yet to be discovered, and there will always be room for further enhancements. Two of the most disruptive technologies, machine learning (ML) and blockchain, have helped replace conventional approaches used in the education sector with highly technical and effective methods. In this study, a system is proposed that combines these two radiant technologies and helps resolve problems such as forgeries of educational records and fake degrees. The idea here is that if these technologies can be merged and a system can be developed that uses blockchain to store student data and ML to accurately predict the future job roles for students after graduation, the problems of further counterfeiting and insecurity in the student achievements can be avoided. Further, ML models will be used to train and predict valid data. This system will provide the university with an official decentralized database of student records who have graduated from there. In addition, this system provides employers with a platform where the educational records of the employees can be verified. Students can share their educational information in their e-portfolios on platforms such as LinkedIn, which is a platform for managing professional profiles. This allows students, companies, and other industries to find approval for student data more easily.

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A machine learning algorithm framework for predicting students performance: A case study of baccalaureate students in Morocco

Cited by 48 publications

References 5 publications

Dashboard stories: How narratives told by predictive analytics reconfigure roles, risk and sociality in education

Dashboard stories: How narratives told by predictive analytics reconfigure roles, risk and sociality in education

An artificial intelligence approach to monitor student performance and devise preventive measures

Integrating machine learning and blockchain to develop a system to veto the forgeries and provide efficient results in education sector

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