Analyzing undergraduate students’ performance in engineering statistics course using educational data mining: Case study in UniMAP

Zakaria, Syed Muhammad Mamduh Syed; Muhamad, Wan Zuki Azman Wan; Azziz, Nor Hizamiyani Abdul

doi:10.1063/1.5054227

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…The prediction of course performance is of utmost importance in the realm of education, as it facilitates the identification of at-risk students, enables tailored counselling and coaching, and optimizes instructional design and development [11,12]. Over the years, numerous studies have been conducted in this field, and they can be broadly categorized into two groups based on the type of data used for prediction.…”

Section: Prediction Of Course Performancementioning

confidence: 99%

Prediction of In-Class Performance Based on MFO-ATTENTION-LSTM

Qin,

Wang,

Yuan

et al. 2024

Int J Comput Intell Syst

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In this paper, we present a novel approach to predicting in-class performance using log data from course learning, which is important in the field of personalized education and classroom management. Specifically, a set of fine-grained features is extracted from unit learning log data to train a prediction model based on long short-term memory (LSTM). However, to enhance the accuracy of the model, we introduce moth flame optimization-attention-LSTM (MFO-Attention-LSTM) as an improvement to the conventional LSTM-attention model. The MFO algorithm is utilized instead of the traditional backward propagation method to calculate attention layer parameters, thereby allowing the model to jump out of local optima. The proposed model outperforms the SVM, CNN, RNN, LSTM, and LSTM-Attention models in terms of the F1 score. Empirical results demonstrate that the optimization of the MFO algorithm contributes significantly to the improved performance of the prediction model. In conclusion, the proposed MFO-Attention-LSTM model offers a promising solution for predicting in-class performance using log data from course learning and could provide valuable insights for personalized education and classroom management.

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Section: Prediction Of Course Performancementioning

confidence: 99%

Prediction of In-Class Performance Based on MFO-ATTENTION-LSTM

Qin,

Wang,

Yuan

et al. 2024

Int J Comput Intell Syst

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“…MDs which correspond to the ACLR will be out of the MS if the MS is appropriately constructed. In other words, the MDs associated with abnormal conditions will have higher values [36,37].…”

Section: The Four Steps In Mtsmentioning

confidence: 99%

Gait Analysis and Mathematical Index-Based Health Management Following Anterior Cruciate Ligament Reconstruction

et al. 2019

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Gait analysis is recognized as a method used in quantifying gait disorders and in clinical evaluations of patients. However, the current guidelines for the evaluation of post anterior cruciate ligament reconstruction (ACLR) patient outcomes are primarily based on qualitative assessments. This study aims to apply gait analyses and mathematical, index-based health management, using the Mahalanobis Taguchi System (MTS) and the Kanri Distance Calculator (KDC) to diagnose the level of the gait abnormality and to identify its contributing factors following ACLR. It is hypothesized that (1) the method is able to discriminate the gait patterns between a healthy group (HG) and patients with ACLR (PG), and (2) several contributing factors may affect ACLR patients’ rehabilitation performance. This study compared the gait of 10 subjects in the PG group with 15 subjects in the HG. The analysis was based on 11 spatiotemporal parameters. Gait data of all subjects were collected in a motion analysis laboratory. The data were then analyzed using MTS and KDC. In this study, two significant groups were recognized: the HG, who achieved results which were within the Mahalanobis space (MS), and (ii) the PG who achieved results above the MS. The results may be seen as being on-target and off-target, respectively. Based on the analysis, three variables (i.e., step width, single support time, and double support time) affected patient performance and resulted in an average mark of above 1.5 Mahalanobis distance (MD). The results indicated that by focusing on the contributing factors that affect the rehabilitation performance of the patients, it is possible to provide individualized and need-based treatment.

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Educational Data Mining

Kandara

Kennedy

2020

Advancing Educational Research With Emerging Technology

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This chapter presents a comprehensive discussion of educational data mining and its potential for educational research. The origins of data mining and the emergence of educational data mining are discussed. The variety of data generated in education (e.g., text, speech, performance, etc.) are described and the challenges of mining these data for useful information are identified. Techniques for mining these data are discussed. Software used to mine these data are noted and issues of theory and ethics are considered. Examples from published literature are cited throughout the chapter and recommendations for educational researchers are offered.

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Analyzing undergraduate students’ performance in engineering statistics course using educational data mining: Case study in UniMAP

Cited by 3 publications

References 11 publications

Prediction of In-Class Performance Based on MFO-ATTENTION-LSTM

Prediction of In-Class Performance Based on MFO-ATTENTION-LSTM

Gait Analysis and Mathematical Index-Based Health Management Following Anterior Cruciate Ligament Reconstruction

Educational Data Mining

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