An Artificial Neural Network Based Early Prediction of Failure-Prone Students in Blended Learning Course

Sukhbaatar, Otgontsetseg; Usagawa, Tsuyoshi; Choimaa, Lodoiravsal

doi:10.3991/ijet.v14i19.10366

Cited by 31 publications

(30 citation statements)

References 24 publications

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“…These are the ones referred to in the following studies addressed in [71][72][73][74][75][76][77][78][79][80][81][82][83][84]. Additionally, the studies referred to in [85,86] include both traditional and online classes. We refer to the ones that explicitly state being conducted with, or including, online classes and assume that the remaining ones were performed in traditional classes.…”

Section: Where Has La Been Deployed In the Studies Produced?mentioning

confidence: 99%

How Does Learning Analytics Contribute to Prevent Students’ Dropout in Higher Education: A Systematic Literature Review

Oliveira

Sobral

Ferreira

et al. 2021

BDCC

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Retention and dropout of higher education students is a subject that must be analysed carefully. Learning analytics can be used to help prevent failure cases. The purpose of this paper is to analyse the scientific production in this area in higher education in journals indexed in Clarivate Analytics’ Web of Science and Elsevier’s Scopus. We use a bibliometric and systematic study to obtain deep knowledge of the referred scientific production. The information gathered allows us to perceive where, how, and in what ways learning analytics has been used in the latest years. By analysing studies performed all over the world, we identify what kinds of data and techniques are used to approach the subject. We propose a feature classification into several categories and subcategories, regarding student and external features. Student features can be seen as personal or academic data, while external factors include information about the university, environment, and support offered to the students. To approach the problems, authors successfully use data mining applied to the identified educational data. We also identify some other concerns, such as privacy issues, that need to be considered in the studies.

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Section: Where Has La Been Deployed In the Studies Produced?mentioning

confidence: 99%

How Does Learning Analytics Contribute to Prevent Students’ Dropout in Higher Education: A Systematic Literature Review

Oliveira

Sobral

Ferreira

et al. 2021

BDCC

View full text Add to dashboard Cite

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“…Besides traditional methods, some studies explore this problem from a deep learning perspective. Multi-layer Perceptrons are utilized in some studies [25]. Fei and Yeung [6] leveraged an LSTM to model student weekly activities recorded by online learning platforms.…”

Section: Related Workmentioning

confidence: 99%

Jointly Modeling Individual Student Behaviors and Social Influence for Prediction Tasks

Liu

Zhu

Zang

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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Prediction tasks about students such as predicting students' academic performances have practical real-world significance at both the student level and the college level. With the rapid construction of smart campuses, colleges not only offer residence and academic programs but also record students' daily life. The digital footprints provide an opportunity to offer better solutions for prediction tasks. In this paper, we aim to propose a general deep neural network which can jointly model student heterogeneous daily behaviors generated from digital footprints and social influence to deal with prediction tasks. To this end, we design a variant of LSTM and a novel attention mechanism to model the daily behavior sequence. The proposed LSTM is able to consider context information (e.g., weather conditions) while modeling the daily behavior sequence. The proposed attention mechanism can dynamically learn the different importance degrees of different days for every student. Based on behavior information, we propose an unsupervised way to construct a social network to model social influence. Moreover, we design a residual network based decoder to model the complex interactions between the features and get the predicted values such as future academic performances. Qualitative and quantitative experiments on two real-world datasets collected from a college have demonstrated the effectiveness of our model.

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“…Among the key tools in the training process are control and measuring materials (hereinafter -CMMs). The latter have not only indicative properties that demonstrate the presence of certain qualities of students, but are also full-fledged didactic means, since the stage of monitoring of learning allows one to fix personal results and correct the process or complete the stage of their formation in a particular subject area [11][12][13]. In light of this approach, CMMs are an essential component of determining the need and sufficiency of organizational and pedagogical conditions for effective professional training.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Methods and Qualimetric Approach to Determine the Conditions for Train Students in the Field of Environmental and Economic Activities

Саламатов

Гафарова

Belevitin

et al. 2021

Int. J. Emerg. Technol. Learn.

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The relevance of environmental and economic activity requires professional training of specialists and, accordingly, new organizational and pedagogical conditions for effective education. It is also necessary to develop control and measuring materials that would have all the qualities (validity, reliability, consistency, significance and objectivity) to obtain the most reliable results in justifying the need and sufficiency of the identified conditions. The intensification of information processes in vocational education leads researchers to the need to find optimal conditions and tools to achieve pedagogical goals. Among these tools are machine learning methods and mathematical models built on their basis for quantitative assessment of the quality of vocational training in the field of environmental and economic activities. The use of the qualimetric approach in pedagogy is possible in the presence of a certain array of observational data for one or another criterion related to learning conditions, personal qualities of students, etc. The construction of an algorithmic model allows one to operate with conditions in mental experiments, test hypotheses, and since pedagogical research is quite long in time, the choice of conditions based on the most favorable forecast built using the model allows one to optimize pedagogical resources to achieve the planned results. Rational selection of effective control and measuring materials (CMMs) allows one to determine the need and sufficiency of organizational and pedagogical conditions. While mathematical modeling allows one to quickly adjust the organizational and pedagogical conditions as a set of opportunities for content, forms, teaching methods, information and communication technologies (ICTs) and CMMs used to achieve the planned educational results in the sphere of environmental and economic activity. Interpretation of the derived features in the context of the pedagogical research performed with a cross-validation accuracy of 72% made it possible to reveal the dominant significance of intersubjective connections between the disciplines studied by the sample of students in the bachelor's and master's programs. Namely, programs 44.03.04 and 44.04.04 "Professional training (by industry)", which are the most significant in terms of the formation of competence in the field of environmental and economic activities. The designed mathematical model of the Gradient Boosting Classifier allows making predictive expectations of the studied competency types and testing hypotheses for the inclusion or exclusion of certain significant organizational and pedagogical conditions for the effective implementation of the educational process. A necessary and sufficient organizational and pedagogical condition for the effective formation of competence in the field of environmental and economic activity is to ensure continuity between significant disciplines and the actualization of interdisciplinary relationships based on the development of interdisciplinary courses.

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An Artificial Neural Network Based Early Prediction of Failure-Prone Students in Blended Learning Course

Cited by 31 publications

References 24 publications

How Does Learning Analytics Contribute to Prevent Students’ Dropout in Higher Education: A Systematic Literature Review

How Does Learning Analytics Contribute to Prevent Students’ Dropout in Higher Education: A Systematic Literature Review

Jointly Modeling Individual Student Behaviors and Social Influence for Prediction Tasks

Machine Learning Methods and Qualimetric Approach to Determine the Conditions for Train Students in the Field of Environmental and Economic Activities

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