Measuring self-regulated learning and online learning events to predict student academic performance

The aim of this study is to identify whether the combination of self-reported data that measure self-regulated learning (SRL) and computer-Assisted data that capture student engagement with an online learning environment could be used to predict student academic achievement. Personally engaged study strategies focused on deep-level learning, the process of taking control, and the evaluation of students' own learning characterize SRL. Diverse theories on how students benefit from SRL underline its positive impact on student academic outcomes. Similarly, there is no doubt that the future trend in education leans towards the integration of technolog y into teaching in order to exploit its full potential. To benefit from both approaches, a combination of self-reported data and detailed online learning events obtained from an online learning environment were investigated in relation to their ability to predict student academic achievement. A case study of 54 university students enrolled in a blended-learning course showed that of the tested SRL variables and observed learning activities, student interaction with auxiliary materials that were part of the course helped to predict academic outcomes. Despite the relatively low ability of the model to explain why some students were able to become successful learners, the presented results highlight the importance of analysing online learning events in computer-Assisted teaching and learning. © 2018 Masaryk University, Faculty of Arts. All rights reserved

Exploring the Effectiveness of AI Algorithms in Predicting and Enhancing Student Engagement in an E-Learning

The shift from traditional to digital learning platforms has highlighted the need for more personalized and engaging student experiences. In response, researchers are investigating AI algorithms' ability to predict and improve e-learning student engagement.  Machine Learning (ML) methods like Decision Trees, Support Vector Machines, and Deep Learning models can predict student engagement using variables like interaction patterns, learning behavior, and academic performance. These AI algorithms have identified at-risk students, enabling early interventions and personalized learning. By providing adaptive content, personalized feedback, and immersive learning environments, some AI methods have increased student engagement. Despite these advances, data privacy, unstructured data, and transparent and interpretable models remain challenges. The review concludes that AI has great potential to improve e-learning outcomes, but these challenges must be addressed for ethical and effective applications. Future research should develop more robust and interpretable AI models, multidimensional engagement metrics, and more comprehensive studies on AI's ethical implications in education

Environmental Factors and Students\u27 Learning Approaches: a Survey on Malaysian Polytechnics Students

Several studies have shown the impact of environmental factors on student learning approaches. Despite the importance of such studies, studies on technical learners are few. Thus, this study aimed to determine the influence of learning environment on Polytechnics students\u27 learning approaches in Malaysia. Learning environment plays an important role in the cognitive, effective and social domains of students because it could improve students\u27 learning outcomes. Learning approaches refer to the ways students deal with academic tasks that are related to learning outcomes. This study used Course Experience Questionnaire (CEQ) and Revised Two-Factor Study Process Questionnaire (RSPQ-2F) to collect the research data. Data were analyzed using AMOS Version 18. Multiple regressions were conducted to predict learning environment factors that influenced the level of students\u27 learning approaches. The result shows that effective teaching is a major factor that influences students\u27 deep approach followed by the assessment, learning resources and clear objectives

Motivation as a predictor of dental students’ affective and behavioral outcomes: Does the quality of motivation matter?

Since the motivation to study and engage in academic activities plays a key role in students’ learning experience and well-being, gaining a better understanding of dental students’ motivations can help educators implement interventions to support students’ optimal motivations. The aim of this study, grounded in self-determination theory, was to determine the predictive role of different types of motivation (autonomous motivation, controlled motivation, and amotivation) in the affective and behavioral outcomes of dental students. Amotivation is the absence of drive to pursue an activity due to a failure to establish relationships between activity and behavior; controlled motivation involves behaving under external pressure or demands; and autonomous motivation is an internalized behavior with a full sense of volition, interest, choice, and self-determination. A cross-sectional correlational study was conducted in 2016, in which 924 students (90.2% response rate) from years one to six agreed to participate, granting permission to access their current GPAs and completing four self-reported questionnaires on academic motivation, study strategies, vitality, and self-esteem. The results showed that self-determined motivation (i.e., autonomous over controlled motivation) was positively associated with vitality, self-esteem, and deep study strategies and negatively associated with surface study strategies. The contrary results were found for amotivation. In the motivational model, deep study strategies showed a positive association with students’ academic performance. Contrary results were found for surface study strategies. This study extends understanding of the differentiation of motivation based on its quality types and suggests that being motivated does not necessarily lead to positive educational outcomes. Autonomous motivation, in contrast to controlled motivation and amotivation, should be supported to benefit students with regard to their approaches to learning and well-being since it can promote students’ vitality, self-esteem, deep over surface study strategies, and enhanced academic performance

Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation

Black-box risk scoring models permeate our lives, yet are typically proprietary or opaque. We propose Distill-and-Compare, a model distillation and comparison approach to audit such models. To gain insight into black-box models, we treat them as teachers, training transparent student models to mimic the risk scores assigned by black-box models. We compare the student model trained with distillation to a second un-distilled transparent model trained on ground-truth outcomes, and use differences between the two models to gain insight into the black-box model. Our approach can be applied in a realistic setting, without probing the black-box model API. We demonstrate the approach on four public data sets: COMPAS, Stop-and-Frisk, Chicago Police, and Lending Club. We also propose a statistical test to determine if a data set is missing key features used to train the black-box model. Our test finds that the ProPublica data is likely missing key feature(s) used in COMPAS.Comment: Camera-ready version for AAAI/ACM AIES 2018. Data and pseudocode at https://github.com/shftan/auditblackbox. Previously titled "Detecting Bias in Black-Box Models Using Transparent Model Distillation". A short version was presented at NIPS 2017 Symposium on Interpretable Machine Learnin

Energy Efficiency Prediction using Artificial Neural Network

Buildings energy consumption is growing gradually and put away around 40% of total energy use. Predicting heating and cooling loads of a building in the initial phase of the design to find out optimal solutions amongst different designs is very important, as ell as in the operating phase after the building has been finished for efficient energy. In this study, an artificial neural network model was designed and developed for predicting heating and cooling loads of a building based on a dataset for building energy performance. The main factors for input variables are: relative compactness, roof area, overall height, surface area, glazing are a, wall area, glazing area distribution of a building, orientation, and the output variables: heating and cooling loads of the building. The dataset used for training are the data published in the literature for various 768 residential buildings. The model was trained and validated, most important factors affecting heating load and cooling load are identified, and the accuracy for the validation was 99.60%