Abstract:Massive open online courses (MOOCs) pose a challenge for instructors when trying to provide personalised support to learners, due to large numbers of registered participants. Conversational agents can be of help to support learners when working with MOOCs. This article presents an adaptive learning module for JavaPAL, a conversational agent that complements a MOOC on Java programming, helping learners review the key concepts of the MOOC. This adaptive learning module adapts the difficulty of the questions prov… Show more
“…Through this study, the importance of teaching presence was highlighted as an antecedent variable affecting the expectancy, value, and cost for MOOC learners. This provides implications for the theoretical foundation and development direction of AI tutor or conversational agent systems related to providing customized feedback on the learners’ responses ( González-Castro et al, 2021 ), or the course recommendation according to the learning status and preference of the learner ( Kim and Kim, 2020 ) in the MOOC environment.…”
Managing learning continuity is critical for successful MOOC learning. Thus, enabling learners to have learning persistence needs to be integrated into the MOOC learning design. Motivation effort is a critical component enabling students to maintain continuous MOOC learning. The expectancy–value theory explains why learners engage in learning: (1) they have a higher perceived ability for learning success, (2) place value on learning, and (3) avoid psychological costs. However, it is unclear how these factors affect MOOC learning persistence and how learners’ motivation is formed from this perspective. This experimental study explored how learners’ motivational variables affect their learning persistence, focusing on the expectancy–value theory. The results of this study indicated that academic self-efficacy and task value had significant positive effects on learning persistence. The structural relationship of antecedent, process, and outcome variables showed that teaching presence as an antecedent had a significantly positive effect on academic self-efficacy and task value. Among the three factors of the expectancy–value theory, only the task value influenced learning persistence through student engagement as a mediator. Based on the results, suggestions are provided for motivating MOOC environments that support learners’ continuous MOOC learning.
“…Through this study, the importance of teaching presence was highlighted as an antecedent variable affecting the expectancy, value, and cost for MOOC learners. This provides implications for the theoretical foundation and development direction of AI tutor or conversational agent systems related to providing customized feedback on the learners’ responses ( González-Castro et al, 2021 ), or the course recommendation according to the learning status and preference of the learner ( Kim and Kim, 2020 ) in the MOOC environment.…”
Managing learning continuity is critical for successful MOOC learning. Thus, enabling learners to have learning persistence needs to be integrated into the MOOC learning design. Motivation effort is a critical component enabling students to maintain continuous MOOC learning. The expectancy–value theory explains why learners engage in learning: (1) they have a higher perceived ability for learning success, (2) place value on learning, and (3) avoid psychological costs. However, it is unclear how these factors affect MOOC learning persistence and how learners’ motivation is formed from this perspective. This experimental study explored how learners’ motivational variables affect their learning persistence, focusing on the expectancy–value theory. The results of this study indicated that academic self-efficacy and task value had significant positive effects on learning persistence. The structural relationship of antecedent, process, and outcome variables showed that teaching presence as an antecedent had a significantly positive effect on academic self-efficacy and task value. Among the three factors of the expectancy–value theory, only the task value influenced learning persistence through student engagement as a mediator. Based on the results, suggestions are provided for motivating MOOC environments that support learners’ continuous MOOC learning.
“…Their next project will be to leverage YouTube as a resource base for implementation. The second example of adaptive information filtering involves a conversational bot that recommends video fragments to learners depending on their answers to questions in a Massive Open Online Course (MOOC) [41]. These video clips are intended to help students better comprehend the idea associated with the question on which they did not get right.…”
Section: Adaptive Information Filteringmentioning
confidence: 99%
“…They can analyse the solution and point out any missing or erroneous information. A good example is given in [41] where a conversational agent analyses the responses that learners in a MOOC are supposed to offer and proposes video segments that cover certain ideas that are not correctly addressed.…”
Section: Intelligent Tutoringmentioning
confidence: 99%
“…Such tutoring activities save learners from having to go through the entire learning material, especially if it is huge, and instead focus on the most important parts. González-Castro et al [41], for example, as part of their design, include a pedagogical agent that proposes video fragments relevant to ideas for questions that a student does not correctly answer in a MOOC.…”
Personalised adaptive learning is becoming increasingly popular as a method of providing each student on an online platform with learning experiences that are tailored to their own requirements and peculiarities. This enables learners to go along many learning routes with the shared objective of information and skill development. In such systems, adaptivity and intelligence play distinct roles, with adaptivity being a more data-driven decision-making approach and intelligence being the emulation of human traits in a learning setting. Pedagogical agents, as defined in the field of artificial intelligence, are virtual characters with anthropomorphic features that are introduced for educational reasons. Because e-learning is a continuously growing area, the responsibilities of pedagogical agents change based on the goals that have been established for them. This article provides a systematic evaluation of pedagogical agents’ research and empirical data in e-learning from 2015 to 2022. Their responsibilities will be examined specifically in terms of flexibility and variety, realistic simulation, and their influence on learning: performance improvement, improved motivation, and engagement. The article finishes with a discussion and recommendations on pedagogical agents’ future directions in this ever-changing world of individualised adaptive e-learning.
“…The adaptive model is the part that reflects the core idea of personalization among the three major components of the adaptive learning system (Martin et al, 2020). Many studies have been conducted to explore the methods of personalized recommendations (Brusilovsky, 2001; González-Castro et al, 2021; Mousavinasab et al, 2021; Paiva et al, 2017; Pardo et al, 2019). As shown in Figure 1 (the circle represents knowledge point, the triangle represents learning material, and the rectangle represents learning strategy), personalized recommendations can be divided into three layers: adaptive learning paths, adaptive learning materials (or items), and adaptive learning strategies.Figure 1.An adaptive learning system architecture featuring an adaptive model.…”
Personalized recommendation plays an important role on content selection during the adaptive learning process. It is always a challenge on how to recommend effective items to improve learning performance. The aim of this study was to examine the feasibility of applying adaptive testing technology for personalized recommendation. We proposed the adaptation of applicable adaptive testing technology as a solution based on the widely accepted teaching philosophy that providing learners with challenging content can stimulate their potential and improve their performance. Specifically, we adapted item selection algorithms to make the difficulty of recommended items above the learner’s current knowledge level on the basis of the uniform scale of learner’s ability and item difficulty in the adaptive testing area. Participants were recruited from two classes in a junior middle school, one served as the experimental group by applying the adaptive testing recommendation, and the other served as the control group by applying the random recommendation. The results showed that the experimental group students achieved significantly higher scores and demonstrated higher learning abilities than the control group students. Therefore, the adapted testing technology that is being used for personalized recommendation is effective in improving learning performance.
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