Dynamic Diagnosis of the Progress and Shortcomings of Student Learning using Machine Learning based on Cognitive, Social, and Emotional Features

Abstract

Student diversity, like academic background, learning styles, career and life goals, ethnicity, age, social and emotional characteristics, course load and work schedule, offers unique opportunities in education, like learning new skills, peer mentoring and example setting. But student diversity can be challenging too as it adds variability in the way in which students learn and progress over time. A single teaching approach is likely to be ineffective and result in students not meeting their potential. Automated support could address limitations of traditional teaching by continuously assessing student learning and implementing needed interventions. This paper discusses a novel methodology based on data analytics and Machine Learning to measure and causally diagnose the progress and shortcomings of student learning, and then utilizes the insight gained on individuals to optimize learning. Diagnosis pertains to dynamic diagnostic formative assessment, which aims to uncover the causes of learning shortcomings. The methodology groups learning difficulties into four categories: recall from memory, concept adjustment, concept modification, and problem decomposition into sub-goals (sub-problems) and concept combination. Data models are predicting the occurrence of each of the four challenge types, as well as a student's learning trajectory. The models can be used to automatically create real-time, student-specific interventions (e.g., learning cues) to address less understood concepts. We envision that the system will enable new adaptive pedagogical approaches to unleash student learning potential through customization of the course material to the background, abilities, situation, and progress of each student; and leveraging diversity-related learning experiences.

Studying Consensus and Disagreement during Problem Solving in Teams through Learning and Response Generation Agents Model

“…Other related applications include optimizing team performance through adjusting team composition, interaction, and behavior [10], or devising training procedures to improve the group activity of certain individuals [11]. Human computer interfaces (HCIs) can also benefit from studying this process, as creating a digital twin representation [12] of a user can help self-improvement by showing opportunities to improve education [13], comfort [14], and growth [8]. For example, ref.…”

“…For example, ref. [13] proposes a system that tracks coding and debugging to create a digital twin of a student's learning programming so that any learning shortcomings are identified and then addressed through customized interventions.…”

Advanced machine learning techniques for personalising technology education

“… Identify Areas of Improvement: Machine learning algorithms can pinpoint areas where students may be struggling or experiencing challenges, such as misconceptions, gaps in knowledge, or learning disabilities. By identifying these areas early on, educators can intervene promptly with targeted interventions and support to address students' needs effectively(Doboli, Doboli, Duke, Hong, & Tang, 2022;Tshidi, 2022).…”