Predicting Student Performance by Using Data Mining Methods for Classification

Kabakchieva, Dorina

doi:10.2478/cait-2013-0006

Cited by 286 publications

(245 citation statements)

References 10 publications

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“…An initial key problem that needs to be addressed is identifying the most suitable method for predicting the performance (Shahiri & Husain, 2015). Following Kabakchieva (2013), several learning algorithms were used in this experiment. These are generalpurpose learning algorithms covering different paradigms.…”

Section: Classification Techniquesmentioning

confidence: 99%

Monitoring Class Activity and Predicting Student Performance Using Moodle Action Log Data

Raga

Raga²

2017

IJCSR

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Purpose -This paper proposes a novel approach for processing course log data obtained from Moodle-based blended courses in order to visualize patterns of student activity within the online environment and to determine whether these log data can be used to predict student academic performance.Method -Logs of student activities were summarized and processed using the Vector Space Model approach. This resulted in a novel vector-based form of representation which can be used to map students' activity in a latent activity space given a set of activity dimensions. An enriched form of this representation was also generated by processing the DateTime and IP address metadata for the purpose of developing classification/predictive model of students' performance. 2Results -The activity space coupled with a one-hot vector representation for each unique activity dimension can be used to visualize the differences in level and type of activity preferences of students. Experiments using several machine learning algorithms indicate that the generated model can modestly distinguish between sets of activities that lead to High, Low, or Failed performances.Conclusion -The development of easily interpretable graphics that can depict trends in student activity is a useful tool for instructors handling blended courses. It can provide constant monitoring of course progression with minimal effort and enable instructors to determine whether and how the environment actually affects student performance.Recommendations -Further work on refining the process applied to the data is recommended. The log data should be time-sliced and processed to determine whether and how the student's level and type of activity changes over time. More powerful machine learning classification techniques shouldalso be tested to determine whether it can improve the classification accuracy.Research Implications -These types of visualizations and predictive models could be used to monitor the student or class which requires immediate and specific pedagogical adjustments.

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Section: Classification Techniquesmentioning

confidence: 99%

Monitoring Class Activity and Predicting Student Performance Using Moodle Action Log Data

Raga

Raga²

2017

IJCSR

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“…Bayesian classifiers are popular classification algorithms due to their simplicity, computational efficiency and very good performance for real-world problems (Kabakchieva, 2013).…”

Section: Discussionmentioning

confidence: 99%

Modeling Students’ Academic Performance Based on Their Interactions with the Online Learning Environment

Akçapınar¹,

Altun²,

Aşkar³

2015

İlköğretim Online

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ABSTRACT. The aim of this study is to model students' academic performance based on their interactions in an online learning environment. The dataset includes 10 input attributes extracted from students' learning interaction data. As an output (class) variable, the final grades obtained from their Computer Hardware course were used. The modeling performance of three different classification algorithms were tested (naïve Bayes classifier, classification tree and CN2 rules) on the dataset. All analyses were performed using the Orange data mining tool, and the models were evaluated using ten-fold cross-validation. The results of analysis were presented as a confusion matrix, a decision tree, and if-then rules. The predictive performance of the algorithms was also tested and compared using the classification accuracy (CA), and area under the ROC Curve (AUC) metrics. The experimental results indicate that the naïve Bayes algorithm outperforms other classification algorithms when compared using the CA and AUC metrics. The naïve Bayes algorithm correctly classified 75.4% of the students according to their grade for the course (Fail, Pass, and Good). The classification model also accurately predicted 81.5% of the students who failed, and 91.8% of the students who passed the course. On the other hand, the classification tree and the CN2 algorithms generated models which can be used with confidence in decision making processes by non-expert data mining users.

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“…They show that explanatory techniques such as decision trees achieve reasonable performance, while pointing out most indicative factors in students' dropping out process. Kabakchieva [14] casts the student course success prediction problem as that of a classification. In addition to past grades, they also employ other factors, e.g., admission scores, as features.…”

Section: Related Workmentioning

confidence: 99%

Predicting Student Success in Courses via Collaborative Filtering

Çakmak

2017

ijisae

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Based on their skills and interests, students' success in courses may differ greatly. Predicting student success in courses before they take them may be important. For instance, students may choose elective courses that they are likely to pass with good grades. Besides, instructors may have an idea about the expected success of students in a class, and may restructure the course organization accordingly. In this paper, we propose a collaborative filtering-based method to estimate the future course grades of students. Besides, we further enhance the standard collaborative filtering by incorporating automated outlier elimination and GPA-based similarity filtering. We evaluate the proposed technique on a real dataset of course grades. The results indicate that we can estimate the student course grades with an average error rate of 0.26, and the proposed enhancements improve the error value by 16%.

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Predicting Student Performance by Using Data Mining Methods for Classification

Cited by 286 publications

References 10 publications

Monitoring Class Activity and Predicting Student Performance Using Moodle Action Log Data

Monitoring Class Activity and Predicting Student Performance Using Moodle Action Log Data

Modeling Students’ Academic Performance Based on Their Interactions with the Online Learning Environment

Predicting Student Success in Courses via Collaborative Filtering

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