Temporal learning analytics for computer based testing

Papamitsiou, Zacharoula; Terzis, Vasileios; Economides, Anastasios A.

doi:10.1145/2567574.2567609

Cited by 29 publications

(57 citation statements)

References 20 publications

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“…Shahiri & Husain (2015) conducted a systematic literature review to observe the characteristics substantially contributing to the prediction of class-room performances. Cumulative grade point (CGPA) and assessments (such as assignments and quiz marks) were considered to be the two primary attributes to assess students' performance (Elakia & Aarthi, 2014;Mayilvaganan & Kalpanadevi, 2014;Papamitsiou et al, 2014;Tucker et al, 2014). Another perspective encompasses factors of students' legacy data (such as past performances in previous assessments/entry test etc.)…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting academic performance of students from VLE big data using deep learning models

Waheed

Hassan

Aljohani

et al. 2020

Computers in Human Behavior

330

145

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This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Section: Literature Reviewmentioning

confidence: 99%

Predicting academic performance of students from VLE big data using deep learning models

Waheed

Hassan

Aljohani

et al. 2020

Computers in Human Behavior

330

145

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“…All attributes will be grouped in one attribute called internal assessment. The attributes are mostly used among the researchers to predict students performance [5,17,18,19,20,21,10,22,23,12].…”

Section: The Important Attributes Used In Predicting Student's Performentioning

confidence: 99%

A Review on Predicting Student's Performance Using Data Mining Techniques

Shahiri

Husain

Rashid

2015

Procedia Computer Science

633

349

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Predicting students performance becomes more challenging due to the large volume of data in educational databases. Currently in Malaysia, the lack of existing system to analyze and monitor the student progress and performance is not being addressed. There are two main reasons of why this is happening. First, the study on existing prediction methods is still insufficient to identify the most suitable methods for predicting the performance of students in Malaysian institutions. Second is due to the lack of investigations on the factors affecting students achievements in particular courses within Malaysian context. Therefore, a systematical literature review on predicting student performance by using data mining techniques is proposed to improve students achievements. The main objective of this paper is to provide an overview on the data mining techniques that have been used to predict students performance. This paper also focuses on how the prediction algorithm can be used to identify the most important attributes in a students data. We could actually improve students achievement and success more effectively in an efficient way using educational data mining techniques. It could bring the benefits and impacts to students, educators and academic institutions.

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“…For the needs of our research, we configured a simplified version of the LAERS assessment environment (Papamitsiou, Terzis, & Economides, 2014). We implemented a testing mechanism and a tracker that logs student temporal activity data during testing.…”

Section: Methodsmentioning

confidence: 99%

“…We also embedded a questionnaire into the system in order to gather self-reported data. Initial results (Papamitsiou, Terzis, & Economides, 2014) highlighted a detected trend that TTAC and TTAW have a significant direct positive and negative effect on AP respectively, and they both have a significant direct positive effect on (un-)certainty. Moreover, GE is a determinant of TTAC and TTAW, and (un-)certainty is a determinant of AP as well.…”

Section: Contribution and Work In Progressmentioning

confidence: 96%

Temporal Learning Analytics for Adaptive Assessment

Papamitsiou

Economides

2014

Learning Analytics

Self Cite

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Accurate and early predictions of student performance could significantly affect interventions during teaching and assessment, which gradually could lead to improved learning outcomes. In our research, we seek to identify and formalize temporal parameters as predictors of performance ("temporal learning analytics" or TLA) and examine students' temporal behaviour during testing (i.e., in terms of time-spent). The goal is to specify a functional set of parameters that will be embedded in an adaptive assessment system in order to contribute towards the personalization of feedback services. In this paper, we present the motivation and rationale for our work, along with our methodology, initial results, contributions so far, and plans for future work.

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Temporal learning analytics for computer based testing

Cited by 29 publications

References 20 publications

Predicting academic performance of students from VLE big data using deep learning models

Predicting academic performance of students from VLE big data using deep learning models

A Review on Predicting Student's Performance Using Data Mining Techniques

Temporal Learning Analytics for Adaptive Assessment

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