Application of machine learning and data mining in predicting the performance of intermediate and secondary education level student

Yousafzai, Bashir Khan; Hayat, Maqsood; Afzal, Sher

doi:10.1007/s10639-020-10189-1

Cited by 53 publications

(29 citation statements)

References 17 publications

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“…In addition to the prediction of final grades, learning analytics in high school was used to predict school dropout ( Lakkaraju et al, 2015 ; Filho and Adeodato, 2019 ; Baker et al, 2020 ), discovering clues to avoid middle school failure at early stages ( Jiménez-Gómez et al, 2015 ), and to assist the education department or policymakers to predict the number of graduating and dropout students ( Yousafzai et al, 2020 ).…”

Section: Resultsmentioning

confidence: 99%

“…In terms of prediction learning outcomes, Yousafzai et al (2020) used supervised machine learning techniques with students’ demographics information and results of previous exams to predict the students’ overall performance in Pakistan. The classifiers reached an accuracy higher than 95%.…”

Section: Resultsmentioning

confidence: 99%

“…The majority of the papers retrieved in this SLR did not explicitly highlight any challenges of the application of learning analytics in high schools. However, the main issues raised were related to data quality, especially when considering different sources of data (Filho and Adeodato, 2019;Yousafzai et al, 2020), and privacy concerns (d'Anjou et al, 2019;Jadav et al, 2017).…”

Section: Rq5: What Are the Challenges In Using Learningmentioning

confidence: 99%

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Applications of Learning Analytics in High Schools: A Systematic Literature Review

Sousa

Alexandre

Mello

et al. 2021

Front. Artif. Intell.

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Learning analytics aims to analyze data from students and learning environments to support learning at different levels. Although learning analytics is a recent field, it reached a high level of maturity, especially in its applications for higher education. However, little of the research in learning analytics targets other educational levels, such as high school. This paper reports the results of a systematic literature review (SLR) focused on the adoption of learning analytics in high schools. More specifically, the SLR followed four steps: the search, selection of relevant studies, critical assessment, and the extraction of the relevant field, which included the main goals, approaches, techniques, and challenges of adopting learning analytics in high school. The results show that, in this context, learning analytics applications are focused on small-scale initiatives rather than institutional adoption. Based on the findings of this study, in combination with the literature, this paper proposes future directions of research and development in order to scale up learning analytics applications in high schools.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Rq5: What Are the Challenges In Using Learningmentioning

confidence: 99%

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Applications of Learning Analytics in High Schools: A Systematic Literature Review

Sousa

Alexandre

Mello

et al. 2021

Front. Artif. Intell.

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“…Kaunang and Rotikan (2018) produced several models based on decision tree algorithm over a data containing student's demographics, academic and family background features collected through questionnaires. Yousafzai et al (2020) applied decision tree and regression algorithms over the historic performance of students and proposed a system to forecast students' grades.…”

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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“…K-nearest neighbors (KNN) is a technique of non-parametric pattern recognition that utilizes the average of the nearest K observations in the training set to provide an approximation. KNN can be applied to classification (Cover & Hart., 2006;Dudani, 1976) and regression problems (Yousafzai et al, 2020). In order to Fig.…”

Section: K-nearest Neighborsmentioning

confidence: 99%

Predicting the percentage of student placement: A comparative study of machine learning algorithms

Çakıt

Dağdeviren

2021

Educ Inf Technol

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In recent years, there has been an increase in the demand for higher education in Turkey, where the demand, as in most other countries, exceeds what is available. The main purpose of this research is to develop machine learning algorithms for predicting the percentage of student placement based on the data related to the university's academic reputation, opportunities of the city where the university is located, facilities and cultural opportunities of the university. When the model accuracy was evaluated on the basis of performance metrics, the Extreme Gradient Boosting (XGBoost) algorithm showed greater predictive accuracy than other machine learning approaches. A sensitivity analysis was performed using the extreme gradient boosting machines algorithm to identify the degree to which the input variables contribute to the determination of the output variable. Five input variables, namely the percentage of student placement at year t-1, the university scientific document score, university Phd programme score, university faculty member/student score, and the percentage of student placement at year t-2 were found to be the most effective parameters. Prediction and sensitivity analysis results obtained in this study can be used in many different ways, such as determining the quotas for universities, allocating resources, and making new regulations.

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Application of machine learning and data mining in predicting the performance of intermediate and secondary education level student

Cited by 53 publications

References 17 publications

Applications of Learning Analytics in High Schools: A Systematic Literature Review

Applications of Learning Analytics in High Schools: A Systematic Literature Review

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

Predicting the percentage of student placement: A comparative study of machine learning algorithms

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