Research Article
Kiran Fahd, Shah J. Miah
CONT ED TECHNOLOGY, Volume 17, Issue 4, Article No: ep606
ABSTRACT
The increasing reliance on the learning management system (LMS) in this era of digital education offers a vital source of student data that can be leveraged to predict student academic progress. Predicting student academic progress in higher education (HE) supports timely intervention and enhances student retention. This study develops and compares multiple machine learning (ML) and deep learning (DL) models to identify at-risk students based on students’ interaction data with LMS by leveraging an integrated DSR methodology. Multiple predictive models are developed by incorporating data augmentation and balancing techniques to address class imbalance and enhance the accuracy of the predictive model. The study compares ten different models to achieve the highest classification accuracy in predicting students at risk of failing through the integration of through the integration of both ML and DL algorithms, including random forest, decision tree, convolutional neural networks, multi-layer perceptron, and long short-term memory (LSTM). The comparison results unscored the value of the DL based predictive model in the HE setting to precisely predict student academic performance, particularly the LTSM based model, which has the highest and nearly perfect accuracy. The existing LMS systems can incorporate this DL based predictive model to provide educational stakeholders with benefits and insights that support students’ academic journeys and institutional success.
Keywords: machine learning, deep learning, design research, higher education, LMS data, student academic progress
Research Article
Monica W. Tracey, Michael Joiner, Sara Kacin, Jay Burmeister
CONT ED TECHNOLOGY, Volume 9, Issue 2, pp. 186-205
ABSTRACT
Instructional design focuses on solving problems in a multitude of contexts. As such, designers are investigators, gathering evidence to optimally design solutions to learning problems within the identified context. The challenge described in this case study was the need to create an educational activity to promote interaction and collaboration among an interdisciplinary participant group comprised of physicians, radiobiologists, and radiation physicists. Based on the premise that interdisciplinary medical research collaboration requires a shared understanding of authentic problems from multiple perspectives, this design research case documents the design and implementation of an online case study incorporating collaborative inquiry in interdisciplinary teams with the intended outcome of building or strengthening interdisciplinary communication skills. Contextual factors – including the design team and design process – influencing the design of the activity are documented. Results indicate that using an interactive online case study as the basis for collaborative inquiry in small, interdisciplinary teams followed by a summative, large group discussion resulted in (1) evidence-based treatment decisions based on the data supplied in the case study and (2) participation of all disciplines in team interactions. Outcomes also indicated the building or strengthening of interdisciplinary communication skills and the understanding of the value and contribution of all three fields to radiation oncology treatment resulted in the participation of the online case study.
Keywords: Collaborative educational intervention, Design research, Instructional design, Conjecture mapping