ABSTRACT
Automated feedback is an essential section of the digital learning platform in providing insights into the assistance of students in terms of improving their mathematical proficiency levels and achieving learning goals. The COVID-19 pandemic has had a very significant impact on the learning and assessment experiences of students across the globe. In an online learning context, automated feedback allows teachers to customize learning content and its assessments according to the student's needs. Following this line of reasoning, the researchers began their research by designing and verifying the quality of an automated system of digital learning platform that measures mathematical proficiency levels. Therefore, this research aims to prove the effectiveness of the automated feedback system of digital learning platforms by importing students' e-MAT-Testing results into it. A design-based research design along with three phases was employed in this research. A total of 517 seventh-grade students and six experts participated in this research. The 517 seventh-grade students were selected using a stratified random sampling technique to participate in an e-MAT-Testing that consisted of 18 items on the topic of measurement and geometry. The test results were utilized to design a mathematical proficiencies assessment tool consisting of five sections, namely data acquisition, processing, display, automated feedback, and assessment report. However, the researchers would like to focus on the effectiveness of the automated feedback systems only to fit the real setting of the six experts who were purposively selected. The results of the first phase showed that there are five levels for both mathematical proficiency dimensions. The intersections for mathematical process dimension were at-1.41,-0.69, 0.49, and 1.39 while the intersections for conceptual structure dimension were at-0.98, 0.14, 0.44, and 1.70. The automated feedback system is found important for the students because they can check their test scores automatically while the teachers can use the data as guidelines for grading purposes. Moreover, the heuristic results indicated that the automated feedback system of the digital learning platform was found effective in terms of its usefulness, interpretation, and accuracy at the most appropriate levels from the six experts' perspectives. Copyright © 2022, Anka Publishers. All rights reserved.
ABSTRACT
Worldwide, computer science departments have experienced a dramatic increase in the number of student enrolments. Moreover, the ongoing COVID-19 pandemic requires institutions to radically replace the traditional way of on-site teaching, moving interaction from physical to virtual space. We report on our strategies and experience tackling these issues as part of a Haskell-based functional programming and verification course, accommodating over 2000 students in the course of two semesters. Among other things, we fostered engagement with weekly programming competitions and creative homework projects, workshops with industry partners, and collaborative pair-programming tutorials. To offer such an extensive programme to hundreds of students, we automated feedback for programming as well as inductive proof exercises. We explain and share our tools and exercises so that they can be reused by other educators. © K. Kappelmann, J. Rädle & L. Stevens.
ABSTRACT
The COVID-19 pandemic has reformed the teaching-learning processes in engineering education across the globe. Virtual classrooms substituted physical classrooms with the widespread use of online meeting platforms. The proliferation of virtual classrooms not only paved the way for accelerated digital transformation but also brought back some elementary issues in engineering education. Many engineering students face difficulties in comprehending the fundamental concepts in their courses during virtual learning. As real-world engineering solutions depend on conceptual clarity, misconceptions of basic engineering principles need to be taken seriously. If not identified, analysed and corrected with constructive feedback, misconceptions on various engineering topics can create challenging obstacles in learning. Against this backdrop, this research study introduces a novel solution titled Classification of Students Misconceptions in Individualised Learning Environment (C-SMILE). The primary objective of the C-SMILE system is to examine the usefulness of personalised automated feedback to students to enhance their conceptual understanding by pinpointing their misconceptions. Besides, we propose a method by which students' misconceptions can be effectively classified for every instructional objective in every engineering course using machine learning techniques. Our pilot-study results show that the proposed C-SMILE system can precisely classify students' misconceptions in engineering education settings. © 2022 IEEE.