A Multilayer Prediction Approach for the Student Cognitive Skills Measurement

Ahmad, Shair; Li, Kan; Amin, Adnan; Anwar, Muhammad Shahid; Khan, Wahab

doi:10.1109/access.2018.2873608

Cited by 18 publications

(11 citation statements)

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“…This work focuses on prediction of CS in terms of students' performance prediction, i.e., individual score. We are partially inspired by those literature findings of psychology, neuroscience and cognitive science which have statistically linked student's performance with study schedules and student's biological factors [24]. Also, literature is saturated with the number of CS prediction approaches that focused on the computation of an individual's performance during cognitive tasks.…”

Section: Prediction Of Cognitive Skills Via Students Performancementioning

confidence: 99%

“…The age group consists of six layers while each of the other two factors (gender description and parent's cohabitation status) comprised of two layers. Second, to discover the depth of the CS, we classify it into 20 outcome variables (0 ≤ CS ≤ 10, with a period of 0.5) which is referred to as component-wise quantization of CS [24]. CS is decomposed into many pieces, which are usually known as knowledge component [25], [26]; therefore, splitting CS range into multiple intervals facilitate the proposed system to detect the robustness of the CS.…”

Section: Introductionmentioning

confidence: 99%

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Deep Network for the Iterative Estimations of Students’ Cognitive Skills

et al. 2020

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The effective educational systems aimed at improving Cognitive Skills (CS) both in and out of institutions. Such system relies on timely prediction of CS during students' activities in intuitions. Meanwhile, literature is saturated with the number of approaches which have used study schedules, biological and environmental factors to predict CS. However, the loopholes in prior studies have become the main source of inspiration for the current attempt. In this study, we propose a Bayesian Neural Network which predicts CS by iterative manipulations of CS under the profound influence of Student's Basic Attributes. Initially, the study classifies the Basic Attributes into three factors (1.age group, 2. gender, and 3. parent's cohabitation status) which have multiple layers. Furthermore, the technique splits the range of CS into 20 periodic outcome variables (with a period of 0.5). Eventually, the network iteratively estimates each outcome of CS by feed-forward process through Basic Attributes layers. We have reviewed the performance of the proposed network by using a students' score dataset. The results have illustrated that the current technique obtained significant prediction accuracy in terms of accuracy measures.

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Section: Prediction Of Cognitive Skills Via Students Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Network for the Iterative Estimations of Students’ Cognitive Skills

et al. 2020

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“…Ngoài ra, mô hình dự đoán khả năng bỏ học của sinh viên sử dụng các kĩ thuật khai phá dữ liệu và trên cơ sở đó lập kế hoạch ngăn cản tình trạng này đã được nhóm tác giả nghiên cứu trong [4]. Một cách tiếp cận dự đoán đa lớp để đo lường kĩ năng nhận thức của sinh viên được đề cập trong [5]. Kết quả nghiên cứu cho thấy phương pháp tiếp cận này tối ưu hơn so với các kĩ thuật đo lường kĩ năng nhận thức của sinh viên hiện có với độ chính xác đạt 0.979.…”

Section: Giới Thiệuunclassified

An Overview of Educational Data Mining

Nguyen¹,

Ha²

2019

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Higher education data is growing, but the exploitation and extraction of meaningful knowledge for management have not been paid much attention. The existing mining tools are not effective. This study aims to introduce three techniques for educational data mining: (1) Classification techniques, (2) Predictive models, (3) Clustering techniques. Simultaneously, the study also proposes some solutions to analyze and visualize data, predict students’ learning capacity and assemble learners. Thereby, education managers could choose appropriate data mining solutions for effective management and training.

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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%

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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A Multilayer Prediction Approach for the Student Cognitive Skills Measurement

Cited by 18 publications

References 40 publications

Deep Network for the Iterative Estimations of Students’ Cognitive Skills

Deep Network for the Iterative Estimations of Students’ Cognitive Skills

An Overview of Educational Data Mining

Academic Performance Prediction Based on Multisource, Multifeature Behavioral Data

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