Enhancing Video-based Learning Using Knowledge Tracing: Personalizing Students’ Learning Experience with ORBITS (2023)

Abstract: As the world regains its footing following the COVID-19 pandemic, academia is striving to consolidate the gains made in students’ education experience. New technologies such as video-based learning have shown some early improvement in student learning and engagement. In this paper, we present ORBITS predictive engine at YOURIKA company, a video-based student support platform powered by knowledge tracing. In an exploratory case study of one master’s level Speech Processing course at the Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) in Abu Dhabi, half the students used the system while the other half did not. Student qualitative feedback was universally positive and compared the system favorably against current available methods. These findings support the use of artificial intelligence techniques to improve the student learning experience.

## introduction looking ahead to the post-pandemic tertiary education landscape, higher education institutions ought to innovate by incorporating effective technology to maximize a more personalized video-based learning experience. one of "the most recurring challenges towards online learning" @xcite 2) is disengagement and absence of participation. a poll of 350 university students taking synchronous (fully online) zoom classes indicated that approximately 94% had moderate to considerable difficulty with online learning @xcite . recently, investigators have examined the effects of videobased learning (vbl) on student learning and engagement @xcite . @xcite and @xcite have for instance established that it can have a positive impact on learning, and "students' perceptions of video effectiveness significantly predicted how they perceived the overall effectiveness of the course" @xcite . studies by @xcite on first year osteopathic students in australia and @xcite on three b.sc. undergraduate student groups (microbiology) in ireland have also indicated that vbl stimulated interest and improved performance, motivation, and engagement, as it increased exam confidence and decreased exam anxiety. an additional benefit of vbl was "improved communication with their mentees and greater ability to demonstrate the experiments to their student groups" @xcite . so far, however, there has been little discussion in the published literature about the use of ai-powered video-based learning support platforms at the postgraduate level to personalize learning paths and improve the achievement of intended learning outcomes. knowledge tracing involves creating models that track students' understanding over time, enabling accurate predictions of their future performance. advancements in this area would allow personalized resources to be recommended based on individual needs, while identifying content that may be too easy or challenging, allowing for skipping or postponing. machine learning solutions could potentially extend the benefits of high-quality personalized instruction to anyone worldwide, at no cost. ## methodology this work proposes a personalized learning environment named orbits as shown in figure 1 that gathers and traces the knowledge state of the learner. we built a new model that is beyond the standard deep learning-based model based on long short-term memory (lstm) with a new network architecture and a combination of methodologies to solve the challenges in question. beyond the standard approach mentioned above, we built the following four methods: ## results this section illustrates the different methods that are used to improve the results. ## discussion defining the input sequence and the way it is architected in the lstm is the key to defining how the lstm is used. hence, we used lstm in a different manner by defining the sequence in two ways: horizontally across topics and vertically across learning time per topic. the topic-based encoding provides a sequence of topics based on their dependencies as prerequisites. student knowledge context provides the chronological order of the student learning sequence in each topic over time and hence, across topics. this unique sequence definition enabled us to go beyond the standard approach by using lstm in a unique manner in knowledge tracing. these two sequences would not have been possible without (1) the question to topic mapping that enabled us to work at the level of the topic rather than questions, and (2) the representative subset of the feature space that enabled the capability of selecting prerequisite topics. ## conclusion the aim of the present research was to examine student use of and engagement with orbits and the extent to which orbits predictive engine improved students' perception of their learning experience. positive initial feedback from participants indicated that orbits's ease of use, responsiveness, and usefulness were the three main factors of learning satisfaction. in fact, previous research has shown that learning satisfaction often correlates with performance and achievement of learning outcomes. based on our initial exploratory findings, we can conclude that students using orbits are more engaged, more satisfied with their learning experience and may achieve higher assessed learning outcomes than students not using orbits.