Predicting Student Performance in Solving Parameterized Exercises

Sahebi, Shaghayegh; Huang, Yun; Brusilovsky, Peter

doi:10.1007/978-3-319-07221-0_62

Cited by 9 publications

(2 citation statements)

References 9 publications

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“…The job requires operation of the correlation between auxiliary and target resource type. Better performance will be possible merely by employing other task information [9]. Wang et al viewed the student performance prediction as a short term sequential behaviour.…”

Section: Literature Reviewmentioning

confidence: 99%

A Predictive Model for Student Employability Using Deep Learning Techniques

Ambili¹,

Abraham²

2022

ECS Trans.

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Education in the present scenario is outcome based and focuses mainly on the skill sets a student acquires on completion of the studies. The society is increasingly concerned about the quality of programs, international rankings, and placement statistics of HEI (Higher Education Institutions). This study concentrates on how demographic data, scholastic and co-scholastic abilities of students, faculty characteristics, and teaching practices contribute to the student learning. Dataset pertaining to the study were collected from the same institution for which the placement prediction needs to be calculated. The study models the problem as a sequential event prediction problem and employs deep learning techniques. The proposed model extracts data from dataset with 18 attributes. This predictive approach evaluates the performance of lower level and higher order skills and provide the enhancement methods by which a student can be on the path to full-time employment before leaving the campus.

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Section: Literature Reviewmentioning

confidence: 99%

A Predictive Model for Student Employability Using Deep Learning Techniques

Ambili¹,

Abraham²

2022

ECS Trans.

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“…Besides the pioneer models BKT and PFA, techniques of recommend system have also been utilized for students' performance prediction. Comparisons have been made among PFA, BKT, BPMF and BPTF [6] in the parametric questions to discuss the temporal influence [7].…”

Section: Introductionmentioning

confidence: 99%

Improve the Prediction of Student Performance with Hint's Assistance Based on an Efficient Non-Negative Factorization

Liu

Sun

et al. 2017

IEICE Trans. Inf. & Syst.

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In tutoring systems, students are more likely to utilize hints to assist their decisions about difficult or confusing problems. In the meanwhile, students with weaker knowledge mastery tend to choose more hints than others with stronger knowledge mastery. Hints are important assistances to help students deal with questions. Students can learn from hints and enhance their knowledge about questions. In this paper we firstly use hints alone to build a model named Hints-Model to predict student performance. In addition, matrix factorization (MF) has been prevalent in educational fields to predict student performance, which is derived from their success in collaborative filtering (CF) for recommender systems (RS). While there is another factorization method named non-negative matrix factorization (NMF) which has been developed over one decade, and has additional non-negative constrains on the factorization matrices. Considering the sparseness of the original matrix and the efficiency, we can utilize an element-based matrix factorization called regularized single-element-based NMF (RSNMF). We compared the results of different factorization methods to their combination with Hints-Model. From the experiment results on two datasets, we can find the combination of RSNMF with Hints-Model has achieved significant improvement and obtains the best result. We have also compared the Hints-Model with the pioneer approach performance factor analysis (PFA), and the outcomes show that the former method exceeds the later one.

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Predicting Learning Performance in Serious Games

Kickmeier-Rust¹

2018

Serious Games

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Predicting Student Performance in Solving Parameterized Exercises

Cited by 9 publications

References 9 publications

A Predictive Model for Student Employability Using Deep Learning Techniques

A Predictive Model for Student Employability Using Deep Learning Techniques

Improve the Prediction of Student Performance with Hint's Assistance Based on an Efficient Non-Negative Factorization

Predicting Learning Performance in Serious Games

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