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Pre-evaluation of the learner's level is a common learning strategy designed to determine the prior knowledge and skills of learners. A pre-evaluation is carried out at the beginning of the course and based on the results obtained, personalized resources will be provided that respond to individual learner needs. This paper presents a pre-evaluation...
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... fulfillment webhook is a service that allows a dynamic response by searching for response elements in an external database. At the Webhook stage, questions and answers are processed, the learner's level is detected (learner's classification as shown in Figure 2), a recommendation and a personalized feedback are provided to the learner. The webhook records all this information and data in the Mongodb database. ...
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... Moodle, as a widely adopted open source platform, stands out for its functionalities aligned with the principles of UDL and differentiated pedagogy. Indeed, several studies have shown that Moodle improves learner satisfaction, performance and engagement thanks to its collaborative tools and pedagogical adaptation options (Gamage et al., 2022;Kaiss et al., 2023;Safsouf et al., 2020;Yilmaz, 2022;Evardo Jr and Itaas, 2024). In particular, Moodle enables: ...
The purpose of this study is to investigate how an adaptive assessment pathway can contribute to promoting personalized learning and improving access to education for all learners, regardless of their individual learning styles, paces, or needs. The study suggests a personalized learning assessment plan incorporating differentiated pedagogy and Universal Design for Learning (UDL). We conduct the experiment using the Moodle platform, taking advantage of Information and Communication Technologies (ICT) to reach a larger number of learners. The research used a mixed methodology to qualitatively analyze traditional learning assessment pathways’ limitations and quantitatively examine the proposed adaptive pathway’s impact on learning outcomes. While the results show a significant improvement in learning outcomes (88.9% improvement in Text study, 50% in Language activity, 55.6% in Writing, 80.6% in Total Control, and 77.8% in Oral production), the study also highlights the need for further research into the mechanisms, strategies, tools, approaches, issues, and future prospects associated with learning assessment.
... Chatbots can be classified into three subtypes according to their goals: personal or impersonal, domain-specific or non-domain-specific, and task-, information-, or conversation-based, as references [22]- [25] show. In addition, other authors introduce the concept of AI programs; as shown in references [26]- [28], users can develop human-computer interaction technologies capable of handling natural language understanding (NLU). ...
... In the method developed by [6], the rapid prototyping approach was used to present a lab based on AI speakers and the ADDIE model, which focuses on formative assessment. On the other hand, in [26], a conversational chatbot uses the Moodle platform. The use of methods such as NLP and deep learning (DL) makes it possible to integrate strategies such as remediation into massive open online courses (MOOCs). ...
... For example, ChatGPT is a learning tool for undergraduate students, according to [23], [35], and [36]. For assessment, researchers in [26] and [25] have developed a tool that allows students to be placed at the appropriate level, while researchers in [13] have developed an evaluation tool for high school students. Additionally, the authors in [40] have shown a tool that allows for the evaluation of student cognitive presence. ...
Chatbots are emerging technologies with the potential to improve teaching and learning processes. This paper conducts a systematic review of research on chatbots in education, focusing on articles published in Online-Journals.org from 2011 to 2024. The aim is to examine the various aspects addressed by the authors, such as design principles, pedagogical roles, interaction styles, and evaluation methods for chatbots in educational contexts. The tools were classified according to the type of user they targeted, revealing that 42% were aimed at students, 11% at teachers, 29% at both types of users, and 18% at external users. The characteristics of the tools along the above dimensions were analyzed, highlighting trends, good practices, and observed limitations. The key findings, challenges, and implications of using chatbots to improve learning outcomes, and experiences were discussed. It was concluded that chatbots are an emerging technology that offers benefits such as teaching personalization, self-learning, and real-time feedback but also poses challenges, such as evaluation and research into their effectiveness for education.
... QuizCbot (conversational chatbot) [92], STAAR online platform [89], speech assessment for Moodle (SAM) [88], TestVision [118], Python [182], [183], OJ system [162], CPR Tutor [163], CBA Tool [86], CoFee [164], BLSTM [184], EvoGrader [185], artificial intelligence (AI) [165]- [167], [186], EnglishCentral [88], eDia [77], [168]- [170], Socrative [83], [85], [134], Moodle [72], [78], [130], [135]- [138], Kahoot! [79], [84], [142], GAMET [96], [187], Blackboard: LMS [93], [140], [141], [143], [144], R program [91], [94], [95], [110], [114], [188]- [192], Android-based gamification [147], ANNOTA platform [193], ARTE [96], Automarker [172], BILOG-MG [62], [66], [67], Computer-assisted knowledge graph analysis [173], Custom GPT (GPT-4) [165], Dewis [80], E-assessment system [150], ELLA-Math CBA system [153], GPT-3 [186], Lectora online [144], MCAT [97], OpenCT [98], Optical identify (OID) [175], Quest [91], [104], TAACO [96], video-based communication assessment (VCA) [90], and writeAlizer [187]. ...
... QuizCbot (conversational chatbot) [92], Mini-CEX WebApp [123], CBA tool [86], eDia [77], [168]- [170], Blackboard: LMS [93], [140], [141], [143], [144], Dewis [80], PhysTHOTS-CAT [156], SEAKMAP [159], Tryout application [74], and video-based communication assessment (VCA) [90]. Response time recording CBA tool [86] Test item generation Python [183] and MATrix LABoratory (MATLAB) with the Symbolic Math Toolbox extension [206] 3. ...
... Students given information about the correction feedback can identify concepts they have not mastered properly and make mistakes in them. Similarly, Kaiss et al. [92] conducted a study that included a PDF format report containing answers to quiz questions and explanations, allowing students to evaluate their work after taking the test. As for the assessor, such as a teacher, the system can provide an assessment report detailing feedback related to six different inductive processes, which can be downloaded by the teacher [77]. ...
Previous studies have demonstrated that technology helps achieve learning outcomes. However, many studies focus on just one aspect of technology's role in educational assessment practices, leaving a gap in studies that examine how various aspects affect the use of technology in assessments. Hence, through a systematic work, we analyzed the extent and manner in which technology is integrated into educational assessments and how education level, domain of learning, and region may affect the use of technology. We reviewed empirical studies from two major databases (i.e., Scopus and ERIC) and a national journal whose focus and scope are on educational measurement and assessment, following PRISMA guidelines for systematic reviews. The findings of the present study are directed towards emphasizing the roles of technology in educational assessment practices and how these roles are adapted to varying educational contexts such as the level of education, the three domains of learning (i.e., cognitive, psychomotor, and affective), and the setting in which the assessment was conducted. These findings not only highlight the current roles of technology in educational assessment but also provide a roadmap for future research aimed at optimizing the integration of technology across diverse educational contexts.
... This combination improves the precision and relevance of responses, which are essential features in an educational environment. The development of the chatbot responds to the need for more adaptable, accurate, and fast systems in learning management platforms, such as Moodle, which educational institutions around the world use [10]. While Moodle has integrated chatbots to facilitate interaction between students and teachers, current solutions fail to offer comprehensive support in the learning process. ...
Chatbots in educational settings have grown significantly, facilitating interaction between students and learning platforms. However, current systems, such as Rasa, Moodle Integrated Chatbots, and ChatterBot, present significant limitations in precision, adaptability, and response time, affecting their effectiveness in resolving academic queries and personalizing learning. To address these shortcomings, this work proposes the development of an advanced educational chatbot that combines large language models (LLMs) with knowledge graphs, allowing for more accurate and contextualized responses and offering valuable suggestions to enrich the learning process. The system is evaluated based on its ability to adjust to different student profiles and offer fast and accurate responses. The results show that the proposed chatbot achieves a precision of 85%, outperforming Rasa and ChatterBot, which achieved accuracies of 83% and 81%, respectively. Furthermore, the chatbot reduces response times to 0.41 seconds, improving efficiency compared to other solutions. The system also demonstrates adaptability, effectively adjusting to students’ learning styles and academic levels. This work indicates that knowledge graph integration and hyperparameter optimization are crucial to improving educational chatbots’ precision, speed, and adaptability, presenting an innovative solution that overcomes the limitations of current systems.
... Identifying Moodle's strengths in establishing engaging learning environments is critical for properly using its potential (Kaiss, Mansouri, & Poirier, 2023). Educators may optimize Moodle's use and adjust their teaching tactics by understanding the features, capabilities, and tools that contribute to learner engagement. ...
This study surveyed 51 lecturers from 11 universities in Ghana and Nigeria to gauge their perceptions of Moodle Learning Management System's (LMS) ability to create engaging learning environments within the PEBL West Africa program. Participants from diverse academic backgrounds were selected through convenience sampling.
Using an online survey based on the Technology Acceptance Model (TAM), the research covered multiple facets of Moodle. Analysis included descriptive statistics and advanced methods, revealing consistently positive views across demographics. Lecturers' positive perceptions of Moodle correlated with its efficacy in promoting learner engagement.
Implications highlight Moodle's pivotal role in enhancing teaching and learning experiences in West African universities. Emphasizing ongoing support for educators is crucial to maximizing Moodle's potential. This study provides valuable insights for educators, administrators, and policymakers, guiding efforts to improve blended learning practices. Future research can explore strategies to further leverage Moodle's potential for enhanced learner engagement and improved academic outcomes in the region.
An adaptive learning system aims to provide learning that is adapted to a learner’s current status, different from the traditional classroom experience. A key element of an adaptive learning system is the recommendation system, which provides the most suitable resources based on learner profiles. Recommending the most appropriate learning resources to learners has always been a challenge in the field of e-learning. Thus, learners may have difficulties in choosing the appropriate material when faced with a large volume of recommended material during their learning process. This challenge led us to implement a chatbot to help learners improve their learning experience and knowledge. New solutions use artificial intelligence (AI) techniques such as machine learning (ML) and natural language processing (NLP). The use of our chatbot integrated in Moodle, named LearningPartnerBot, provides learners a personalized recommendation of learning objects according to two strategies, one based on their knowledge level (KL) and the other based on their learning style (LS). The objective of this article is to compare the learning outcomes obtained after the realization of the two experiments based on these two approaches centered mainly on the use of the LearningPartnerBot. Consequently, the approach of recommending learning objects based on the knowledge level gave promising results by guaranteeing a more adapted learning to the learners.KeywordsE-learningLearning Object RecommendationExperimental DesignAdaptive LearningChatbot
