Fuzzy-based active learning for predicting student academic performance using autoML: a step-wise approach

Tsiakmaki, Maria; Kostopoulos, Georgios; Kotsiantis, Sotiris; Ragos, Omiros

doi:10.1007/s12528-021-09279-x

Cited by 17 publications

(6 citation statements)

References 51 publications

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“…Tsiakmaki et al introduce a fuzzy‐based active learning method for predicting students' academic performance which combines, in a modular way, autoML practices. Their experimental results revealing that the proposed method for the accurate prediction of students at risk of failure has better performance compared with the classical classifier 130 . Hussain et al proposed a method to predict students' performance by combining unsupervised clustering and association rule mining methods with supervised classification methods 77 …”

Section: Resultsmentioning

confidence: 99%

A survey on educational data mining methods used for predicting students' performance

Xiao

2021

Engineering Reports

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Predicting students' performance is one of the most important issues in educational data mining (EDM), which has received more and more attention. By predicting students' performance, we can identify students' risk of academic failure and help instructors to take some actions such as guidance or interventions to help learners as early as possible, or carry out continual evaluation of learners as to optimize learning path or personalized learning resources recommendation. In this survey, we reviewed the 80 important studies on predicting students' performance using EDM methods in 2016-2021, synthesized the procedure of building prediction model of students' performance which contains four phases and 10 key steps, and compared and discussed the latest EDM methods used in all steps. We analyzed the challenges faced by previous studies in three aspects and put forward future suggestions on data collection, EDM methods used, and interpretation of prediction model. This survey provides a comprehensive understanding and practical guide for researchers in this field, and also provides direction for further research.

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Section: Resultsmentioning

confidence: 99%

A survey on educational data mining methods used for predicting students' performance

Xiao

2021

Engineering Reports

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“…Kotsiantis, S. has 10 out of 12 publications on predicting student performance using different models or machine learning algorithms. For instance, the studies Alachiotis et al (2022) and Tsiakmaki et al (2021) use fuzzy logic-based automated machine learning and supervised machine learning methods to predict student performance, respectively. Alachiotis et al (2022) also showed that through a voting generalization procedure involving three of the most accurate classifiers and the default parameters of learning algorithms, the prediction outcome is much higher and more accurate than only using a single-tuned learning algorithm.…”

Section: Current Research Areas and Authors For Collaborationmentioning

confidence: 99%

Bibliometric insights into data mining in education research: A decade in review

Rao,

Chen

2024

CONT ED TECHNOLOGY

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This bibliometric study on data mining in education synonymous with big educational data utilizes VOSviewer and Harzing’s Publish and Perish to analyze the metadata of 1,439 journal articles found in Scopus from 2010 to 2022. As bibliometric analyses in this field are lacking, this study aims to provide a comprehensive outlook on the current developments and impact of research in this field. This study employs descriptive and trends analysis, co-authorship analysis, co-citation analysis, co-occurrences of keywords, terms map analysis, and analysis of the impact and performance of publications. It also partially replicates a similar study conducted by Wang et al. (2022), who used the Web of Science (WoS) database. The study is reported in an article entitled ‘Big data and data mining in education: A bibliometrics study from 2010 to 2022’. Results show that data mining in education is a growing research field. There is also a significant difference between the publications in Scopus and WoS. The study found several research areas and topics, such as student academic performance prediction, e-learning, machine learning, and innovative data mining techniques, to be the core basis for collaborating and continuing current research in this field. These results highlight the importance of continuing research on data mining in education, guiding future research in tackling educational challenges.

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“…Predictive analytics may have the potential to support the broader science of learning and guide pedagogical practices in learning and instruction. A lot of studies have been carried out that revealed the efficiency of methods for the accurate prediction of students at risk of failure, such as (a) fuzzy-based active learning methods for predicting students' academic performance, which combines, in a modular way, AutoML (Automated Machine Learning Algorithms) practices [8]; (b) semi-supervised classification tasks for student performance or student dropout prediction [9]; and (c) the comparison of the co-training method with semi-supervised and supervised methods [10].…”

Section: Introductionmentioning

confidence: 99%

Assisting Educational Analytics with AutoML Functionalities

2022

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The plethora of changes that have taken place in policy formulations on higher education in recent years in Greece has led to unification, the abolition of departments or technological educational institutions (TEI) and mergers at universities. As a result, many students are required to complete their studies in departments of the abolished TEI. Dropout or a delay in graduation is a significant problem that results from newly joined students at the university, in addition to the provision of studies. There are various reasons for this, with student performance during studies being one of the major contributing factors. This study was aimed at predicting the time required for weak students to pass their courses so as to allow the university to develop strategic programs that will help them improve performance and graduate in time. This paper presents various components of educational data mining incorporating a new state-of-the-art strategy, called AutoML, which is used to find the best models and parameters and is capable of predicting the length of time required for students to pass their courses using their past course performance and academic information. A dataset of 23,687 “Computer Networking” module students was used to train and evaluate the classification of a model developed in the KNIME Analytics (open source) data science platform. The accuracy of the model was measured using well-known evaluation criteria, such as precision, recall, and F-measure. The model was applied to data related to three basic courses and correctly predicted approximately 92% of students’ performance and, specifically, students who are likely to drop out or experience a delay before graduating.

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Fuzzy-based active learning for predicting student academic performance using autoML: a step-wise approach

Cited by 17 publications

References 51 publications

A survey on educational data mining methods used for predicting students' performance

A survey on educational data mining methods used for predicting students' performance

Bibliometric insights into data mining in education research: A decade in review

Assisting Educational Analytics with AutoML Functionalities

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