Predicting Students’ Performance by Learning Analytics

The field of Learning Analytics (LA) has many applications in today’s technology and online driven education. Learning Analytics is a multidisciplinary topic for learn- ing purposes that uses machine learning, statistic, and visualization techniques [1]. We can harness academic performance data of various components in a course, along with the data background of each student (learner), and other features that might affect his/her academic performance. This collected data then can be fed to a sys- tem with the task to predict the final academic performance of the student, e.g., the final grade. Moreover, it allows students to monitor and self-assess their progress throughout their studies and periodically perform a self-evaluation. From the edu- cators’ perspective, predicting student grades can help them be proactive, in guiding students towards areas that need improvement. Moreover, this study also takes into consideration social factors that might affect students’ performance

Interaction patterns in exploratory learning environments for mathematics: a sequential analysis of feedback and external representations in Chinese schools

Feedback in exploratory learning systems has been depicted as an important contributor to encourage exploration. However, few studies have explored learners’ interaction patterns associated with feedback and the use of external representations in exploratory learning environments. This study used Fractions Lab, an exploratory learning environment for mathematics, to facilitate children’s conceptual understanding of fractions in three Chinese schools. Students (n = 189) from six different classes were invited to use Fractions Lab, and 260,000 event logs were collected. Beyond demonstrating the overall efficacy of the approach, lag sequential analysis supported us in approaching a deeper understanding of patterns of interaction. The findings highlight that the design of three-levels of feedback (Socratic, guidance, and didactic-procedural feedback) played different roles in supporting students to use external representations to perform mathematical tasks in an exploratory learning environment. This study sheds light on how these interaction patterns might be applied to the Fractions Lab system in order to provide increasingly tailored support, based on cultural differences, to enhance students’ technology-mediated learning experiences

UNIFORM: Automatic Alignment of Open Learning Datasets

Learning Analytics aims at supporting the understanding of learning mechanisms and their effects by means of data-driven strategies. LA approaches commonly face two big challenges: first, due to privacy reasons, most of the analyzed data are not in the public domain. Secondly, the open data collections, which come from diverse learning contexts, are quite heterogeneous. Therefore, the research findings are not easily reproducible and the publicly available datasets are often too small to enable further data analytics. To overcome these issues, there is an increasing need for integrating open learning data into unified models. This paper proposes UNIFORM, an open relational database integrating various learning data sources. It presents also a machine learning supported approach to automatically extending the integrated dataset as soon as new data sources become available. The proposed approach exploits a classifier to predict attribute alignments based on the correlations among the corresponding textual attribute descriptions. The integration phase has reached a promising quality level on most of the analyzed benchmark datasets. Furthermore, the usability of the UNIFORM data model has been demonstrated in a real case study, where the integrated data have been exploited to support learners’ outcome prediction. The F1-score achieved on the integrated data is approximately 30% higher than those obtained on the original data

The FuturICT education accelerator

Education is a major force for economic and social wellbeing. Despite high aspirations, education at all levels can be expensive and ineffective. Three Grand Challenges are identified: (1) enable people to learn orders of magnitude more effectively, (2) enable people to learn at orders of magnitude less cost, and (3) demonstrate success by exemplary interdisciplinary education in complex systems science. A ten year ‘man-on-the-moon’ project is proposed in which FuturICT’s unique combination of Complexity, Social and Computing Sciences could provide an urgently needed transdisciplinary language for making sense of educational systems. In close dialogue with educational theory and practice, and grounded in the emerging data science and learning analytics paradigms, this will translate into practical tools (both analytical and computational) for researchers, practitioners and leaders; generative principles for resilient educational ecosystems; and innovation for radically scalable, yet personalised, learner engagement and assessment. The proposed Education Accelerator will serve as a ‘wind tunnel’ for testing these ideas in the context of real educational programmes, with an international virtual campus delivering complex systems education exploiting the new understanding of complex, social, computationally enhanced organisational structure developed within FuturICT

The FuturICT education accelerator

Education is a major force for economic and social wellbeing. Despite high aspirations, education at all levels can be expensive and ineffective. Three Grand Challenges are identified: (1) enable people to learn orders of magnitude more effectively, (2) enable people to learn at orders of magnitude less cost, and (3) demonstrate success by exemplary interdisciplinary education in complex systems science. A ten year ‘man-on-the-moon’ project is proposed in which FuturICT’s unique combination of Complexity, Social and Computing Sciences could provide an urgently needed transdisciplinary language for making sense of educational systems. In close dialogue with educational theory and practice, and grounded in the emerging data science and learning analytics paradigms, this will translate into practical tools (both analytical and computational) for researchers, practitioners and leaders; generative principles for resilient educational ecosystems; and innovation for radically scalable, yet personalised, learner engagement and assessment. The proposed Education Accelerator will serve as a ‘wind tunnel’ for testing these ideas in the context of real educational programmes, with an international virtual campus delivering complex systems education exploiting the new understanding of complex, social, computationally enhanced organisational structure developed within FuturICT

A Deep Learning Approach Towards Student Performance Prediction in Online Courses: Challenges Based on a Global Perspective

Analyzing and evaluating students' progress in any learning environment is stressful and time consuming if done using traditional analysis methods. This is further exasperated by the increasing number of students due to the shift of focus toward integrating the Internet technologies in education and the focus of academic institutions on moving toward e-Learning, blended, or online learning models. As a result, the topic of student performance prediction has become a vibrant research area in recent years. To address this, machine learning and data mining techniques have emerged as a viable solution. To that end, this work proposes the use of deep learning techniques (CNN and RNN-LSTM) to predict the students' performance at the midpoint stage of the online course delivery using three distinct datasets collected from three different regions of the world. Experimental results show that deep learning models have promising performance as they outperform other optimized traditional ML models in two of the three considered datasets while also having comparable performance for the third dataset.Comment: Accepted and presented in 24th International Arab Conference on Information Technology (ACIT'2023

Self-organizin map clustering method for the analysis of e-learning activities

Students‘ interactions with e-learning vary according to their behaviours which in turn, yield different effects to their academic performance. Some students participate in all online activities while some students participate partially based on their learning behaviours. It is therefore important for the lecturers to know the behaviours of their students. But this cannot be done manually due to the unstructured raw data in students‘ log file. Understanding individual student‘s learning behaviour is tedious. To solve the problem, data mining approach is required to extract valuable information from the huge raw data. This research investigated the performance of Self-organizing Map (SOM) to analyze students‘ elearning activities with the aim to identify clusters of students who use the e-learning environment in similar ways from the log files of their actions as input. A study on Meaningful Learning Characteristics and its significance on students‘ leaning behaviors were carried out using multiple regression analysis. Then SOM clustering technique was used to group the students into three clusters where each cluster contains students who interact with the E-learning in similar ways. Behaviors of students in each cluster were analyzed and their effects on their learning success were discovered. The analysis shows that students in Cluster1 have the highest number of interactions with the e-learning (Very Active), and having the highest final score mean of 91.12%. Students in Cluster2 have less number of interactions than that of Cluster1 and have final score mean of 75.65%. Finally, students Cluster3 have least number of interactions than the remaining clusters with final score means is 36.57%. The research shows that, students who participate more in Forum activities emerged the overall in learning success, while students with lowest records on interactions have lowest performance. The research can be used for early identification of low learners to improve their mode of interactions with e-learning.MUSA WAKIL BAR

An immersive virtual reality learning environment with CFD simulations: Unveiling the Virtual Garage concept

Virtual reality has become a significant asset to diversify the existing toolkit supporting engineering education and training. The cognitive and behavioral advantages of virtual reality (VR) can help lecturers reduce entry barriers to concepts that students struggle with. Computational fluid dynamics (CFD) simulations are imperative tools intensively utilized in the design and analysis of chemical engineering problems. Although CFD simulation tools can be directly applied in engineering education, they bring several challenges in the implementation and operation for both students and lecturers. In this study, we develop the “Virtual Garage” as a task-centered educational VR application with CFD simulations to tackle these challenges. The Virtual Garage is composed of a holistic immersive virtual reality experience to educate students with a real-life engineering problem solved by CFD simulation data. The prototype is tested by graduate students (n = 24) assessing usability, user experience, task load and simulator sickness via standardized questionnaires together with self-reported metrics and a semi-structured interview. Results show that the Virtual Garage is well-received by participants. We identify features that can further leverage the quality of the VR experience with CFD simulations. Implications are incorporated throughout the study to provide practical guidance for developers and practitioners

Virtual, augmented reality and learning analytics impact on learners, and educators: A systematic review

Virtual and Augmented Reality technologies have emerged as promising tools in the education sector, offering new possibilities for immersive learning experiences. Many researchers have focused their research on examining the potential of these technologies in education from different perspectives. However, it was discovered that there are research gaps in current systematic reviews regarding the examination of the impact of Virtual, Augmented Reality and Learning Analytics utilization on various types of learners and educators across different educational systems, including K-12 Education, Higher Education, Vocational, and Industrial Training, in addition to the educational systems’ research tendencies and their adoption of these technologies. Therefore, our study aims to address these gaps by searching various studies in Google Scholar, Scopus, and the IEEE Xplore databases. By following the PRISMA protocol, 150 research papers were selected for analysis, and our findings show that improving motivation and attention, improving learners’ understanding & performance, and increasing knowledge retention are the most significant impacts on all types of learners. For educators, we found that these technologies have a prominent effect on assisting educators in teaching and training and reducing the burden. Furthermore, we discovered that Higher Education and Augmented Reality were the dominant educational system and the technology type in the selected studies. We also found that most Virtual and Augmented reality researchers preferred to use questionnaires and online surveys for data collection. We further identified that analyzing learners’ traces when interacting with Virtual and Augmented Reality applications can improve learners’ performance and learning experience. Our review offers valuable insights into how integrating these technologies with Learning Analytics can benefit learners and educators and how educational institutions and industrial organizations can take advantage of adopting these technologies