Competency-Based E-Learning Systems: Automated Integration of User Competency Portfolio

Abstract

In today’s learning environment, e-learning systems are becoming a necessity. A competency-based student portfolio system is also gaining popularity. Due to the variety of e-learning systems and the increasing mobility of students between different learning institutions or e-learning systems, a higher level of automated competency portfolio integration is required. Increasing mobility and complexity makes manual mapping of student competencies unsustainable. The purpose of this paper is to automate the mapping of e-learning system competencies with student-gained competencies from other systems. Natural language processing, text similarity estimation, and fuzzy logic applications were used to implement the automated mapping process. Multiple cases have been tested to determine the effectiveness of the proposed solution. The solution has been shown to be able to accurately predict the coverage of system course competency by students’ course competency with an accuracy of approximately 77%. As it is not possible to achieve 100% mapping accuracy, the competency mapping should be executed semi-automatically by applying the proposed solution to obtain the initial mapping, and then manually revising the results as necessary. When compared to a fully manual mapping of competencies, it reduces workload and increases resource sustainability.

Innovative Methodologies and Approaches to Teaching with Artificial Intelligence in Ukrainian Higher Education

“…Analysing the research devoted to AI-based methodology in higher education, it was found that six teaching approaches are effectively applied. They include: differentiated instruction (Büyüksoy et al, 2020;Eikeland & Ohna, 2022), student-centered approach (Kerimbayev et al, 2023;Liao et al, 2021), technology-based approach (Kozlova & Pikhart, 2021;Zawacki-Richter et al, 2019), group learning (Kim et al, 2022b;Seo at al., 2021;Tan et al, 2022), game-based learning (Priyaadharshini et al, 2020;Videnovik et al, 2023), and competency-based approach (Margienė et al, 2022;Oroszi, 2020). Figure 2 shows the teaching approached in higher education with AI.…”

“…According to Margienė et al (2022), Oroszi (2020), and Sistermans (2020), the competency-based approach means using outcome-based practice and it is aimed towards the increase of knowledge, skills, and values required for carrying out professional tasks adequately. The competence-based approach focuses on development and assessment of professional skills and competencies of future specialists.…”

“…In addition, the competency-based approach focuses on the students' learning outcomes considering their professional interest and needs (Sistermans, 2020). AI assisted to create competency-based environment (Margienė et al, 2022) and requires to use certain methods including case study, project-based learning, peer learning, preparation of presentations, role-plays, storytelling, simulations, work with video, micro-teaching, conceptual learning tasks.…”

“…Approaches to teaching with AI Technology-based approach ensures the integration of teaching technology into the educational environment (Kozlova & Pikhart, 2021;Zawacki-Richter et al, 2019) to conduct questionnaire about the students' preferences on technologies, particularly AI-driven tool; to consider two tiers of implementation of technologybased approach where the first one deals wit technology as a tool to solve the educational tasks and the second onetechnology as a process itself; to develop different tasks that demand using technologies; to keep students engaged; to provide all learning resources in electronic format Competency-based approach is related to outcome-based practice and oriented towards the increase of knowledge, skills, and values required for dealing with professional and everyday situations (Margienė et al, 2022;Oroszi, 2020;Sistermans, 2020) to design the relevant curriculum that is related to formation of professional competency; to apply practical exercises and simulations; to focus upon professional development and complex outcomes; to introduce competency framework; to improve the educational process continually according to changes within the professional sphere Differentiated learning is used to design teaching delivery practices and educational materials considering students' learning styles, interests, to conduct educational data analysis; to develop flexible teaching methods like grouping or student-led activities; abilities, educational needs (Büyüksoy et al, 2020;Eikeland & Ohna, 2022) to adapt instruction according to students' learning styles and needs; to design flexible content; to use ongoing assessment Interactive learning requires students' active participation (Fan & Li, 2023;Seo et al, 2021) to make active learning a priority; to set clear educational objectives for students; to foster students' collaboration; to introduce the peer learning; to implement diverse methods; to gamify the learning; to apply scenario-based tasks with preliminary preparation; to encourage students to the peer assessment and selfassessment Teaching methods with the use of AI Practical exercises allow students to develop new skills, improve learning experience, and change behaviour (Biwer et al, 2020;Yang, 2022) to use demonstration; to discuss the task objectives and rules; to develop reality-based scenario; to study what students already know about the topic and determine what they need to learn; to focus upon professional development; to discuss the possible ways to solve the problem Group activities relate to organised activity of group of students working on the same task (Eager & Brunton, 2023;Kamalov et al, 2023) to introduce the idea of group work to students; to explain the activity; to divide students into heterogeneous groups; to encourage discussion through appropriate questions open-ended; to use teacher-led and student-led activities; to develop flexible methodology; to incorporate tasks based on professio...…”