The Effects of Prior Knowledge on the Use of Adaptive Hypermedia Learning Systems

<span lang="EN-US">A number of weaknesses were demonstrated in the E-learning platforms during the Covid-19 pandemic despite the efforts invested. This has negatively influenced learners' motivation and consequently their performance. With the proliferation of technology and the revolution of information and communication technologies (ICT), learning objects have become new epitomes widely used, accessible, and implemented with educational resources and technological support. The integration of learning objects into E-learning has enhanced educational progress, but during critical periods, it is crucial to ensure pedagogical continuity and learner motivation. Based on this observation, we will propose architecture of a personalized learning object model in the context of an adaptive hypermedia learning system (AHS). The objective of our model is to increase the motivation factor which is a determining element in the success of E-learning, our model aims to improve the performance of the learners in order to avoid the abounding of learning and to promote the attendance of the learners. This will be useful later for any design or development of learning objects in hypermedia learning systems that are adaptive to the needs of the learners and in line with their preferences and profiles throughout the learning process offered by the system. </span>

This paper discusses the design and development of a domain-expert repository management system (DERMs) that supports activities for maintaining the domain and the expert module of an adaptive hypermedia learning system. DERMs involves two main activities: the domain knowledge manager which assists the instructors in maintaining their teaching materials; and the expert knowledge manager which assists the instructors in maintaining the expert knowledge for three different categories of learners namely poor, average and good.

Adaptive hypermedia learning systems can be developed to adapt to a diversity of individual differences. Many studies have been conducted to design systems to adapt to learners’ individual characteristics, such as learning style and cognitive style to facilitate student learning. However, no research has been done specifically regarding the adaptation of hypermedia learning system to gender differences. This chapter therefore attempts to fill this gap by examining the published findings from experimental studies of interaction between gender differences and hypermedia learning. Analysis of findings of the empirical studies leads to a set of principles being proposed to guide adaptive hypermedia learning system design onthe basis of gender differences in relation to (i) adaptive presentation and (ii) adaptive navigation support.

Traditional online learning environments generally use a one-size-fits-all approach, neglecting individual learner needs, goals, and cognitive abilities. Adaptive Educational Hypermedia (AEH) systems emerged to address this by personalizing learning content based on user profiles. The Adaptive Learning System is a computerized system developed to enhance digital interactive educational content teaching and learning. The system’s Adaptive algorithm automatically detects learners’ strengths and weaknesses through the adaptation process and provides personalized learning materials, feedback, and guidance during their learning. Adaptive content allows learners to learn the material at their own pace while providing additional support, evaluation, and resources tailored to each learner. In traditional educational systems, every student is given access to the same learning content regardless of their individual learning needs. Higher education institutions face various challenges when testing and implementing adaptive learning systems for their domain of knowledge. Issues related to technical challenges include processing real-time data, difficulties consolidating learning concepts and solutions into existing learning management systems, and the complexity, ease of use, and usability of adaptive learning systems. However, despite the advancement and technical sophistication in adaptive algorithms, most AEH systems still face significant usability challenges. Learners often struggle with complex interfaces, limited system responsiveness, and poor adaptation to diverse demographics and subject backgrounds. Consequently, the current adaptive systems are underutilized in higher education, with limited empirical evidence on their effectiveness in enhancing student engagement, engagement, quality of experience (QoE), and academic performance. While foundational research work (Brusilovsky, 2001; De Bra et al., 2013) and recent studies (Liu & Lim, 2020; colMOOC project) have shown the importance of adaptive educational hypermedia, yet ongoing issues persist regarding usability, learner control (Paramythis & Loidl-Reisinger, 2004), and empirical validation across varied higher education settings (Latham et al., 2004; Educause, 2018). Systematic investigations into user-controllable personalization and its effects on inclusivity and academic achievement remain limited (Xie et al., 2019; Lundqvist; Warburton, 2019). Furthermore, Li et al. (2021) highlighted that existing systems lack comprehensive cognitive modelling and fail to adequately adapt to individual learner dispositions, which restricts their intelligence and adaptability. This complexity in adaptive systems continues to pose challenges for education overall, as emphasized by Fengying Li, Yifeng He, and Qingshui Xue (2021). Addressing these issues, Dubiel et al. (2022) propose a framework for adaptive user interfaces that integrates the interaction environment to enhance system responsiveness. Therefore, there remains a significant gap in the literature regarding the lack of empirical evaluation of user-controlled personalization, particularly in real-world, multi-disciplinary higher education contexts. Existing research has focused mainly on adaptive algorithmic and technical development, but has not given sufficient attention to practical usability (the lack of effective, usable AEH systems, learner diversity, and actual learner-centered outcomes). Moreover, there is a lack of comprehensive frameworks for evaluating the usability of adaptive educational hypermedia (AEH), and there is limited research on how these adaptive systems can foster inclusivity and fairness among diverse learner populations. Therefore, there is an urgent need to address the usability and complexity problems in AEH systems and to suggest the creation of usable, effective, personalised, learner-centric, and user-controllable AEH systems for higher education environments. This research study addresses these gaps by introducing a personalized, user-controllable Adaptive Educational Hypermedia (AEH) framework specially designed for a Higher Education setting. It evaluates multiple versions of adaptive learning systems across multiple disciplines (English, Malay, Computer Science) revealing significant enhancements in both student achievement and satisfaction. The study not only evaluates multiple versions of adaptive systems but also offers practical recommendations for software developers and educators seeking to implement effective, more intuitive, and learner-centric adaptive systems within real higher educational environments. By exploring the relationships between learner demographics, usability, and academic outcomes, the study promotes inclusivity and equity in digital education. More specifically, it introduces a novel user-controllable personalization feature that empowers learners to customize the learning content to improve their adaptive learning experience, addressing a major limitation found in existing systems. The findings demonstrate significant improvements in student performance and satisfaction with the system’s interface, thereby enhancing subject retention, confirming that adaptive learning systems can effectively improve the academic experience in higher education environments.

Currently, the online learners are increasingly demanding more personalized learning since the web technology, and the learners have individual features of characteristics such as learning goals, experiences, interests, personality traits, learning styles, learning activities, and prior knowledge. A personalized learning process requires an adaptive learning system (ALS). In order to adapt, a learner model is required. Thus, modelling the learner model in an adaptive system environment is a key point to success in recommending the learner. The ontology-based approach was used to model the adaptive learning model in this research. Ontology is a graph structure that consists of a collection of contexts, relationships, and models which related to contexts. The ontology of the learner model enables to produce a description of learner’s properties which contains important information about domain knowledge, learning performance, interests, preference, goal, tasks, and personal traits. Keywords - Personalized Learning, Adaptive Learning System, Ontology, Learner Model

A fuzzy expert system-based adaptive learning approach to improving students’ learning performances by considering affective and cognitive factors

This study investigates the long-term impact of adaptive digital learning ecosystems on students' self-regulated learning (SRL) behaviors and academic persistence. Adaptive learning systems personalize the learning experience by adjusting content and feedback to meet individual students' needs, preferences, and performance. These systems enhance engagement, motivation, and learning outcomes through real-time adjustments and continuous feedback. The research aims to explore how adaptive learning systems influence SRL and academic persistence in university courses over time. Using a longitudinal quantitative design, the study tracks SRL behaviors and academic persistence at multiple points during the semester. Results show significant improvements in SRL behaviors such as goal setting, planning, self-monitoring, and reflection among students engaged with adaptive learning environments. These students exhibited greater autonomy, improved metacognitive awareness, and higher motivation. Additionally, students in adaptive systems demonstrated greater academic persistence, as indicated by more time spent on tasks, higher assignment completion rates, and sustained engagement. The findings suggest that adaptive learning platforms promote SRL and academic persistence by offering personalized, responsive learning experiences. Unlike static, non-adaptive environments, adaptive systems provide dynamic support, enhancing students' ability to regulate their learning and remain engaged despite challenges. The study concludes that adaptive learning systems are vital for long-term academic success, though further research is needed to assess the sustainability of these effects in various educational settings and among diverse student populations.

This study investigated the effect of adding metacognitive prompts before a self-explanation task in video example learning using multimodal data collection techniques such as eye tracking, screen recording. The study also examined whether students’ prior knowledge played a moderating role. A total of 116 undergraduate and graduate students watched three video examples on chemistry topics and completed a self-explanation task in a computer-based learning environment. Participants were assigned randomly to one of two conditions: metacognitive prompts versus no-metacognitive prompts. Those in the metacognitive prompts condition demonstrated higher learning performance, better quality of explanation, and increased attention paid to the learning content than those in the no-metacognitive prompts condition. Moreover, a moderated mediation analysis showed that metacognitive prompts improved students’ self-efficacy and then improved their learning performance. Prior knowledge was found to play a moderating role in the relationship between metacognitive prompts and learning performance. The results of this study show that students with low prior knowledge benefit more from metacognitive prompts added before self-explanation tasks in video-based learning. When using video examples in teaching, it is therefore recommended that students be provided with metacognitive prompts before completing self-explanation tasks, especially those with lower prior knowledge, as this can enhance their resulting self-efficacy, increasing their engagement in self-regulating behaviors and thereby lead to improvements in their later learning performance.

Three different versions of a learning program on trigonometry were compared, a program controlled, non-interactive version (CG), an interactive, conflict inducing version (EG 1), and an interactive one which was supposed to reduce the occurrence of a cognitive conflict regarding the central problem solution (EG 2). Pupils ( N = 101) of a “Realschule” all aged 16 or 17, took part in the study. The pupils were assigned to the three groups by chance. Before the learning session, the pupils' prior knowledge and numeric processing capacity was tested. During the learning session (about 45 minutes) each pupil was working with one of the programs. Immediately after the learning session a post-test, testing the pupils' learning performance, and an affect scale were applied. Six weeks later a follow-up testing the learning performance was carried out. In the post-test, the subjects of the EG 1 achieved better results than the other two groups; nevertheless, the difference was not significant. There was a significant interaction effect: the pupils of the EG 1 with high prior knowledge showed a significantly better performance than those with low prior knowledge. Six weeks later the EG 1 achieved significantly better results than the other two groups. There was no longer an interaction effect. The emotions remembered by the three groups were significantly different. The high interest of the pupils of EG 1 was not responsible for the superiority of this group. The results are discussed in regard to classroom instruction and further research studies.

Students’ prior knowledge contributes to their e-book reading comprehension and might affect the usability of an e-book system. This study explored the influence of university students’ prior knowledge on the usability of an e-book system termed “BookRoll,” which was introduced in the context of an information and technology course in this study. One hundred first-year students (36 male and 64 female students) at a university in central Taiwan participated in this study. Students previewed the lecture slides and used e-book functions such as bookmarks, highlights, and memos during seven weekly lessons. Students’ prior knowledge in the domain of information and technology and the Usefulness, Satisfaction, and Ease of use (USE) dimensions of the BookRoll system were collected before and after the treatment course, respectively. This study revealed that students’ prior domain knowledge had no significant effect on the usability of the e-book system. In general, students’ ratings of BookRoll’s usability were high. Positive qualitative feedback across categories was given by students with high and low prior knowledge; however, some negative but constructive qualitative feedback was also provided. In addition, a short questionnaire to measure the USE dimensions of an e-book system for undergraduate users was developed and provided.

Preparatory or bridging courses are widespread and have a long tradition at universities. They are designed to increase students’ academic success – in particular of students with low prior knowledge – and to reduce dropout rates. However, critics of these short and compact bridging courses complain that preparatory courses are not able to fill in content gaps sufficiently in a few weeks. Despite the high prevalence of university bridging courses, little is currently known about the sustainable learning efficacy of these courses. The aim of this study was to examine the short- and long-term effects of a traditional chemistry bridging course on students’ success in the end of the semester examination of first-year chemistry students. For this purpose, students’ learning outcomes were analyzed at the end of the two-week bridging course of students with different prior knowledge. Furthermore, it was investigated in an intervention-reference-group design whether students’ exam results at the end of the first semester differ from participants of the bridging course and students who did not participate in the course. The results of the study reveal that students with low prior knowledge manage to close their content gaps in just a few weeks and to adjust differences in prior knowledge before starting their studies. At the end of the first semester, bridging course participants achieve significantly better exam results than their fellow students who did not enroll in the bridging course. However, mainly students with high prior knowledge seem to benefit from participating in the longer term. In the case of students with low prior knowledge, participation do not lead to better exam results compared to students without participation. Findings of the study can provide a basis for university teachers as well as university development experts to establish university bridging courses as well as to optimize existing offers.

In-game learning supports aim to help students solve game levels (i.e., game-related supports), and connect to underlying content (i.e., content-related and hybrid supports). Students with different levels of prior knowledge may have different needs for in-game supports. In this study, we designed a 2D physics game with game-related, content-related, and hybrid supports to explore the relationships among students’ prior knowledge, their access of learning supports, learning outcomes, and game performance. Our sample included 199 ninth- to eleventh-grade students from a K-12 school in the southeastern US. Our findings indicated that students, regardless of their degree of prior knowledge, tended to access supports that directly addressed the solution of game levels (game-related supports) rather than those which presented content (content-related and hybrid supports). We found that the more frequently students accessed the hybrid supports, the greater their knowledge acquisition, and the more game levels they solved. We found no significant relations between the access of game-related and content-related supports and students’ learning and game performance. Moreover, students with high prior knowledge tended to use hybrid supports more frequently than those with low prior knowledge. Implications of our findings and suggestions regarding future research are discussed.

IntroductionClinical reasoning has been fostered with varying case formats including the use of virtual patients. Existing literature points to different conclusions regarding which format is most beneficial for learners with diverse levels of prior knowledge. We designed our study to better understand which case format affects clinical reasoning outcomes and cognitive load, dependent on medical students’ prior knowledge.MethodsOverall, 142 medical students (3 rd to 6 th year) were randomly assigned to either a whole case or serial cue case format. Participants worked on eight virtual patients in their respective case format. Outcomes included diagnostic accuracy, knowledge, and cognitive load.ResultsWe found no effect of case format on strategic knowledge scores pre- vs post-test (whole case learning gain = 3, 95% CI. -.01 to .01, serial cue learning gain = 3, 95% CI. -.06 to .00 p = .50). In both case formats, students with high baseline knowledge (determined by median split on the pre-test in conceptual knowledge) benefitted from learning with virtual patients (learning gain in strategic knowledge = 5, 95% CI .03 to .09, p = .01) while students with low prior knowledge did not (learning gain = 0, 95%CI −.02 to .02). We found no difference in diagnostic accuracy between experimental conditions (difference = .44, 95% CI −.96 to .08, p = .22), but diagnostic accuracy was higher for students with high prior knowledge compared to those with low prior knowledge (difference = .8, 95% CI 0.31 to 1.35, p < .01). Students with low prior knowledge experienced higher extraneous cognitive load than students with high prior knowledge (multiple measurements, p < .01).ConclusionsThe whole case and serial cue case formats alone did not affect students’ knowledge gain or diagnostic accuracy. Students with lower knowledge experienced increased cognitive load and appear to have learned less from their interaction with virtual patients. Cognitive load should be taken into account when attempting to help students learn clinical reasoning with virtual patients, especially for students with lower knowledge.

A wealth of researches has indicated that retrieval practice can be used as an effective means to improve memory. Recently the effects of retrieval practice have captured the attention of contemporary researchers who are interested in educational applications of retrieval practice. Some researchers have examined the effectiveness of retrieval practice relative to elaborative studying with concept mapping, and found that practicing retrieval produces greater gains in meaningful learning than elaborative studying with concept mapping which has received comment and query by researchers advocating constructivism who thought, compared with practice retrieval, concept mapping is too complex to master. In view of these criticism, the current research examined the effectiveness of retrieval practice and concept mapping from the perspective of cognitive load, it is hypothesized that the intrinsic load of study task which depends on learners’ prior knowledge level has different influence on retrieval practice and concept mapping, and the concept mapping strategy is more complex than retrieval practice, so it could produce more extraneous load on learner and influence the effectiveness. Two experiments were conducted to test the hypotheses. In experiment 1, 120 college students were engaged in a 2 (prior knowledge level: high vs. low) × 3 (study strategy type: retrieval practice vs. concept mapping and repeated study) between-subjects design of experiment, which was aimed to study the different influence of prior knowledge level on the effectiveness of three types of strategies. In experiment 2, 43 college students participated in a within-subjects design of experiment, each student completed a partial concept mapping of one science text and practiced retrieval of a second text which was designed to study the influence of strategy’s complexity on strategy’s effectiveness. The results indicated that on correct rate of memory retention and transfer, the main effect of prior knowledge level wasn't significant, but there was an interaction between learning strategies and prior knowledge level: under the condition of retrieval practice strategy, there is no significant difference on the correct rate of retention and transfer between participates with high prior knowledge and participates with low prior knowledge; as the concept mapping strategy concerned, the participates with high prior knowledge were significantly higher than participates with low prior knowledge on the correct rate of retention and transfer. In addition, the complexity of the strategy would increase learners' extraneous load, but it didn't have decisive influence on the effectiveness of the strategy. In terms of result, it was concluded that the reason why retrieval practice could produce more memory retention and transfer than concept mapping is not because of that its strategy is easier to be master, it is that the concept mapping strategy was very dependent on learner's prior knowledge level. Relatively, retrieval practice is not dependent on learner's prior knowledge level. The results have some important implications. Firstly, retrieval practice is an effective tool to promote retention and transfer, it indeed can enhance subsequent recall more than concept mapping. Thus, educators should pay more attention to the effectiveness of retrieval practice rather than view it as an assessment device to measure what a student knows. Secondly, the findings support the theory that retrieval practice enhances learning by retrieval-specific mechanisms rather than by elaborative study processes.

The goal of this study was to use eye-tracking and log-file data to investigate the impact of prior knowledge on college students’ (N = 194, with a subset of n = 30 for eye tracking and sequence mining analyses) fixations on (i.e., looking at) self-regulated learning-related areas of interest (i.e., specific locations on the interface) and on the sequences of engaging in cognitive and metacognitive self-regulated learning processes during learning with MetaTutor, an Intelligent Tutoring System that teaches students about the human circulatory system. Results revealed that there were no significant differences in fixations on single areas of interest by the prior knowledge group students were assigned to; however there were significant differences in fixations on pairs of areas of interest, as evidenced by eye-tracking data. Furthermore, there were significant differences in sequential patterns of engaging in cognitive and metacognitive self-regulated learning processes by students’ prior knowledge group, as evidenced from log-file data. Specifically, students with high prior knowledge engaged in processes containing cognitive strategies and metacognitive strategies whereas students with low prior knowledge did not. These results have implications for designing adaptive intelligent tutoring systems that provide individualized scaffolding and feedback based on individual differences, such as levels of prior knowledge.