An Unsupervised Ensemble Clustering Approach for the Analysis of Student Behavioral Patterns

Xiao-yong, LI; Zhang, Yong; Cheng, Huimin; Zhou, Feifei; Yin, Baocai

doi:10.1109/access.2021.3049157

Cited by 32 publications

(21 citation statements)

References 21 publications

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“…By merging DBSCAN and k-means algorithms [13], the authors developed an ensemble unsupervised clustering paradigm for assessing student's behavioural traits. The efficiency of the suggested methodology is assessed by performing research on six categories of behavioural data generated by students at a Beijing university and assess the associations among diverse behavioural traits and student's grade point averages (GPAs).…”

Section: Prior Research and Novelty Of Teamentioning

confidence: 99%

“…K value of the enhanced k-means clustering method may vary depending on the dataset and might result in yielding stronger or even worse outcomes for varied datasets. [13] Proposed the application of DBSCAN and K-means clustering to analyse the behavioural patterns of the students.…”

Section: As Shown In Tablementioning

confidence: 99%

“…The limitation in the study [12] is that the k-value of the enhanced k-means method varies depending on the dataset resulting in yielding stronger or even worse outcomes for varied datasets. The issue of the research [13] arises when applying the suggested method to multisource behavioural characteristics with high dimensions.…”

Section: As Shown In Tablementioning

confidence: 99%

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Integrated Assessment of Teaching Efficacy: A Natural Language Processing Approach

Chandrapati¹,

Rao²

2023

IJACSA

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The most significant component in the education domain is evaluation. Apart from student evaluation, teacher evaluation plays a vital role in the colleges or universities. The implementation of a scientific and appropriate assessment method for enhancing teaching standards in educational institutions is absolutely essential. Conventional teacher assessment techniques have always been bounded to bias and injustice for single dimensional assessment criteria, biased scoring, and ineffective integration. In this regard,it is crucial to develop a specialized teacher evaluation assistant (TEA) system that integrates with some computational intelligence algorithms. This research concentrates on using Natural language processing(NLP) based techniques for empirically analysing teaching effectiveness. We develop a model in which a teacher is evaluated based on the content he delivers during a lecture. Two techniques are employed to evaluate teacher effectiveness using topic modelling and text clustering. By the application of topic modelling, an accuracy of 75% is achieved and text clustering achieved an accuracy of 80%. Thus, the method can effectively be deployed to assess and predict the effectiveness of a teacher's teaching.

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Section: Prior Research and Novelty Of Teamentioning

confidence: 99%

Section: As Shown In Tablementioning

confidence: 99%

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Integrated Assessment of Teaching Efficacy: A Natural Language Processing Approach

Chandrapati¹,

Rao²

2023

IJACSA

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“…It should not only be the evaluation of learning achievement, but should include learning attitude, behavior, and habit in the evaluation system. Li et al judged from three aspects of evaluation view, evaluation method, and evaluation process, and proposed the evaluation model of two-way evaluation subject, comprehensive evaluation method, and multi-way evaluation perspective [ 17 ]. Goh et al‘s school uses test scores to evaluate students in a single way, which causes valuable data resources of wasting.…”

Section: Related Workmentioning

confidence: 99%

Higher Education Management and Student Achievement Assessment Method Based on Clustering Algorithm

Wang

2022

Computational Intelligence and Neuroscience

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Monitoring and guiding instructional management require student performance evaluation. Traditional evaluation and analysis methods based on absolute scores, on the other hand, have certain flaws and are unable to fully reflect the information contained in student performance, thus limiting the impact of student performance evaluation on teaching and learning management. Data mining is regarded as the backbone technology for future information processing, and it introduces a new concept to the way humans use data. Schools must analyse and evaluate the performance of students in the same grade level and secondary school in a timely and staged manner. Clustering is a type of data mining that uses similarity rules to classify sample data into groups with a high degree of similarity. To address the difficulties caused by the wide variation in course difficulty in student performance evaluation, a method based on the K-means clustering algorithm is proposed. The K-means algorithm and the improved K-means algorithm with student information are investigated. The test results showed that the K-means clustering algorithm, the improved algorithm in this paper, and the fast global mean clustering algorithm all cluster the same randomly generated data set with noisy points, but the clustering time of the algorithm in this paper is only 0.04, which has obvious advantages. As a result, the clustering algorithm-based higher education management and student performance evaluation mechanism provides some insights for future research on student learning patterns. It is hoped that instructional administrators will gain a better understanding of students’ learning characteristics so that they can better guide their teaching.

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“…The developed version of the ripper algorithm and C4.5 is a partial decision tree algorithm that does not require global optimization to produce appropriate rules for classification [38]. It helps in building a partial decision tree on different sets of instances and produces rules for decision trees [47]. PART is an algorithm that uses a divide-andconquer mechanism to build a partial C4.5 decision tree in each iteration, i.e., it generates a PART decision list, and makes the best leaf into a rule [44].…”

Section: Part Classifiermentioning

confidence: 99%

Predicting Academic Performance Using an Efficient Model Based on Fusion of Classifiers

et al. 2021

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In the past few years, educational data mining (EDM) has attracted the attention of researchers to enhance the quality of education. Predicting student academic performance is crucial to improving the value of education. Some research studies have been conducted which mainly focused on prediction of students’ performance at higher education. However, research related to performance prediction at the secondary level is scarce, whereas the secondary level tends to be a benchmark to describe students’ learning progress at further educational levels. Students’ failure or poor grades at lower secondary negatively impact them at the higher secondary level. Therefore, early prediction of performance is vital to keep students on a progressive track. This research intended to determine the critical factors that affect the performance of students at the secondary level and to build an efficient classification model through the fusion of single and ensemble-based classifiers for the prediction of academic performance. Firstly, three single classifiers including a Multilayer Perceptron (MLP), J48, and PART were observed along with three well-established ensemble algorithms encompassing Bagging (BAG), MultiBoost (MB), and Voting (VT) independently. To further enhance the performance of the abovementioned classifiers, nine other models were developed by the fusion of single and ensemble-based classifiers. The evaluation results showed that MultiBoost with MLP outperformed the others by achieving 98.7% accuracy, 98.6% precision, recall, and F-score. The study implies that the proposed model could be useful in identifying the academic performance of secondary level students at an early stage to improve the learning outcomes.

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An Unsupervised Ensemble Clustering Approach for the Analysis of Student Behavioral Patterns

Cited by 32 publications

References 21 publications

Integrated Assessment of Teaching Efficacy: A Natural Language Processing Approach

Integrated Assessment of Teaching Efficacy: A Natural Language Processing Approach

Higher Education Management and Student Achievement Assessment Method Based on Clustering Algorithm

Predicting Academic Performance Using an Efficient Model Based on Fusion of Classifiers

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