Predicting Students’ GPA and Developing Intervention Strategies Based on Self-Regulatory Learning Behaviors

Zollanvari, Amin; Kızılırmak, Refik Çağlar; Kho, Yau Hee; Hernández-Torrano, Daniel

doi:10.1109/access.2017.2740980

Cited by 56 publications

(25 citation statements)

References 48 publications

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“…Finally, the last area of application of the AI in the educational context is their use to evaluate, assess or predict the performance of the students. In this line we can find examples of researches that analyze the predictive power of the performance of the students in certain activities on the students' grades [10].…”

Section: Ai In Formal Education Settingsmentioning

confidence: 99%

“…Inside the educational field AI is already present combined with other research tendencies such as Smart Classrooms, Personal Learning Environments or Learning Analytics supporting the adoption of data-driven approaches for the assessment and prediction of the success of the new methodologies and support the decision-making process [9][10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

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How to Measure Teachers' Acceptance of AI-driven Assessment in eLearning

Prieto

Cruz-Benito

Therón

et al. 2019

Proceedings of the Seventh International Conference on Technological Ecosystems for Enhancing Multiculturality

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The use of AI is becoming a growing reality the educational field. One of the activities in which it is beginning to be implemented is the assessment of student achievement. This way, we can find in the literature an increasing number of investigations focused on the possibilities offered by the adoption of AI-driven assessment. However, the use of AI is also a source of concern that raises suspicions in some sectors of our society. In this context, knowing the position of the teachers towards this topic is critical to guarantee the successful development of the field. This paper intends to fill a research gap in the literature by offering a technology adoption model based on TAM to study the factors that condition the use of AI-driven assessment among teachers. To present this model we offer a background on the use of AI in education and the technology acceptance among teachers, as well as the definition of the eight constructs and the relational hypotheses included. Finally, the possibilities of the model and future lines of research are discussed.

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Section: Ai In Formal Education Settingsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

How to Measure Teachers' Acceptance of AI-driven Assessment in eLearning

Prieto

Cruz-Benito

Therón

et al. 2019

Proceedings of the Seventh International Conference on Technological Ecosystems for Enhancing Multiculturality

View full text Add to dashboard Cite

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“…• Lifestyle Behaviors (e.g., eating, physical activity, sleep patterns, social tie, and time management) ; and • Learning Behaviors (e.g., class attendance, study duration, library entry, and online learning) ( [7,8,[23][24][25][26][28][29][30][31][32][33][34][35][36][37][38]). For example, [2] investigated the incremental validity of the Big Five personality traits in predicting college GPA.…”

Section: Introductionmentioning

confidence: 99%

“…By analyzing the effect of the factors influencing academic performance, many systems using data to predict academic performance have been developed in the literature [1][2][3][4]7,8,[13][14][15][16][17][18][19][22][23][24][25][26][27][29][30][31]33,34,[37][38][39][40][41]. For instance, in [8], academic performance was predicted based on passive sensing data and self-reports from students' smart phones.…”

Section: Introductionmentioning

confidence: 99%

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Academic Performance Prediction Based on Multisource, Multifeature Behavioral Data

et al. 2021

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Digital data trails from disparate sources covering different aspects of student life are stored daily in most modern university campuses. However, it remains challenging to (i) combine these data to obtain a holistic view of a student, (ii) use these data to accurately predict academic performance, and (iii) use such predictions to promote positive student engagement with the university. To initially alleviate this problem, in this paper, a model named Augmented Education (AugmentED) is proposed. In our study, (1) first, an experiment is conducted based on a real-world campus dataset of college students (N = 156) that aggregates multisource behavioral data covering not only online and offline learning but also behaviors inside and outside of the classroom. Specifically, to gain in-depth insight into the features leading to excellent or poor performance, metrics measuring the linear and nonlinear behavioral changes (e.g., regularity and stability) of campus lifestyles are estimated; furthermore, features representing dynamic changes in temporal lifestyle patterns are extracted by the means of long short-term memory (LSTM). (2) Second, machine learning-based classification algorithms are developed to predict academic performance. (3) Finally, visualized feedback enabling students (especially at-risk students) to potentially optimize their interactions with the university and achieve a study-life balance is designed. The experiments show that the AugmentED model can predict students' academic performance with high accuracy. INDEX TERMS academic performance prediction, behavioral pattern, digital campus, machine learning (ML), long short-term memory (LSTM)

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