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Chatbot Design to Help Learners Self-Regulte Their Learning in Online Learning Environments
... [8] added that the lack of student engagement is a major difficulty in Higher Education (HE) online learning, which raises the dropout rate. Also, [2,9,10] mentioned that students have struggled with a lack of self-regulation skills to monitor the learning process. Thus, there is a greater need to improve the design and implementation of the teaching and learning process [4,11]. ...
... Thus, there is a greater need to improve the design and implementation of the teaching and learning process [4,11]. In contrast to in-person lectures, where teachers can assist students in managing their own learning, online learning settings requires more autonomy [9] and self-regulated learning skills [2,9,10]. In fact, traditional e-learning systems are limited and unable to meet the needs of every learner. ...
... By defining goals, tracking progress, and adapting learning strategies, students who participate in SRL can take charge of their learning process and improve their motivation, engagement, and academic performance [15,19]. According to [10,15,17] self-regulated students actively search for information when necessary and take the required steps to learn it. They plan, set goals, organize, monitor, and evaluate themselves at various stages of the learning process. ...
Artificial Intelligence (AI) chatbots play an important role in modern
izing education, particularly e-learning platforms by acting as intelligent tools to
address issues such as disengagement and dropout rates. AI chatbots are also used
to captivate students through engaging and enjoyable learning activities. These
chatbots can enhance the pedagogical concept of Self-Regulated Learning (SRL)
by providing personalized support and feedback to students. This work explores
how AI chatbots might increase students’ SRL skills and foster successful learning
outcomes in e-learning environments. Our proposed conceptual framework aims
to link the AI chatbots (technological side) and self-regulated learning (pedagogi
cal side) using the power of academic emotions. In the forethought phase of SRL,
our AI chatbot will guide students in setting short-term goals while detecting their
initial motivation and then monitor students in the following phases of the SRL
process. The framework highlights how AI chatbots using academic emotions can
be used to support learning needs and will serve as a roadmap for future theory
development and empirical investigation.
... Chatbot promises to solve a variety of problems in education today. One of the biggest advantages of chatbot is that it can support students individually and intently (Poirier, et al, 2023). Other research has confirmed that chatbots can be virtual companions for users intended to resolve availability issues, provide support and customer assistance powered by artificial intelligence. ...
... In the higher-education, a chatbot can be trained from a wide variety of resources ranging from learning experiences to learning materials. According to Poirier et al (2023),chatbots are trained to answer common questions about the study of a subject. This motivates learning supports more quickly and conveniently. ...
... In Poirier et al (2023), the use of a chatbot in an educational context for the التكنولوجية والتربية للمناهج الدولية المجلة المجلد الرابع عشر ( العدد والعشرون الخامس ) automation of higher education student care is presented. In this study it is used to self-regulate the learning of the students. ...
... In Ng et al.'s [40] study, the chatbot prompted students to selfevaluate their learning performance and reflect on the effectiveness of their SRL strategies. Similarly, Kaiss et al. [41] developed a chatbot that assisted learners in self-evaluation by administering a test based on a series of questions. The feedback resulting from this test enabled learners to selfevaluate and monitor their progress and achievements. ...
... The distribution of the other two behavioral engagement measures (session length and utterance turns) departed from normality, as revealed by the Shapiro-Wilk tests (p < 0.05). The results of students' behavioral engagement with the goal-setting and review activities are shown in [29,41] [36,72]). 2) Students' Goal-setting, Help-seeking, and Selfevaluation Strategies The response rate for the questionnaire was 100 % (N = 25). ...
In an online learning environment, both instruction and assessments take place virtually where students are primarily responsible for managing their own learning. This requires a high level of self-regulation from students. Many online students, however, lack self-regulation skills and are ill-prepared for autonomous learning which can cause students to feel disengaged from the online activities. In addition, students tend to feel isolated during online activities due to limited social interaction. To address these challenges, this study explores the use of chatbots to facilitate students' self-regulated learning strategies and promote social presence to alleviate their feelings of isolation. Using a two-phase mixed-methods design, this study evaluates students' behavioral engagement, perceived self-regulated learning strategies, and social presence in the chatbot-supported online learning. In the first phase (Stage I Study), thirty-nine students engaged in a goal-setting chatbot activity that employed the SMART framework and social presence indicators. The findings served as the basis for improving the chatbot design in the second phase (Stage II Study), in which twenty-five students interacted with the revised chatbot, focusing on goal-setting, help-seeking, self-evaluation, and social interaction with instructor's presence. The results show that the students in both studies had positive online learning experiences with the chatbots. Follow-up interviews with students and instructors provide valuable insights and suggestions for refining the chatbot design, such as chatbots for ongoing monitoring of self-regulation habits and personalized social interaction. Drawing from the evidence, we discuss a set of chatbot design principles that support students' self-regulated learning and social presence in online settings.
... Moodle для онлайн-обучения [8]. Схема бота достаточно сложная и позволяет реализовать такие функции, как целеполагание, планирование, тайм-менеджмент, самомониторинг, самооценивание и автоматизированная обратная связь. ...
... In [9], the authors have proposed a methodology to improve the quality of e-learning, chatbot architectural design, to help learners self-regulate their learning by accompanying them via a chatbot within the Moodle platform, which constitutes a metacognitive virtual assistant. ...
The demand for effective learning tools and platforms in the field of web development and coding has been steadily rising. This survey paper explores the development of a Learning Management System (LMS) integrated with an AI assistant, like ChatGPT, aimed at enhancing the learning process and efficiency for learners. The LMS includes innovative features such as document-based and video-based learning modules, comprehensive step-by-step project guides, career roadmaps, and problem-solving and interview preparation resources. The primary objective of this project is to assess the impact of AI chatbots like ChatGPT in accelerating the learning process for coding and app development. It evaluates the LMS's capability to equip learners with the skills and knowledge required to build web applications and secure employment in the field. The study examines the effectiveness of each feature within the LMS, contributing to a holistic understanding of how technology can be leveraged to expedite the learning journey and improve learners' employability.
E-Learning has become more and more popular in recent years with the advance of new technologies. Using their mobile devices, people can expand their knowledge anytime and anywhere. ELearning also makes it possible for people to manage their learning progression freely and follow their own learning style. However, studies show that E-Learning can cause the user to experience feelings of isolation and detachment due to the lack of human-like interactions in most E-Learning platforms. These feelings could reduce the user’s motivation to learn. In this paper, we explore and evaluate how well current chatbot technologies assist users’ learning on E-Learning platforms and how these technologies could possibly reduce problems such as feelings of isolation and detachment. For evaluation, we specifically designed a chatbot to be an E-Learning assistant. The NLP core of our chatbot is based on two different models: a retrieval-based model and a QANet model. We designed this two-model hybrid chatbot to be used alongside an E-Learning platform. The core response context of our chatbot is not only designed with course materials in mind but also everyday conversation and chitchat, which make it feel more like a human companion. Experiment and questionnaire evaluation results show that chatbots could be helpful in learning and could potentially reduce E-Learning users’ feelings of isolation and detachment. Our chatbot also performed better than the teacher counselling service in the E-Learning platform on which the chatbot is based.
By all accounts, 2016 is the year of the chatbot. Some commentators take the view that chatbot technology will be so disruptive that it will eliminate the need for websites and apps. But chatbots have a long history. So what's new, and what's different this time? And is there an opportunity here to improve how our industry does technology transfer?
Educational researchers have begun recently to identify and study key processes through which students self-regulate their academic learning. In this overview, I present a general definition of self-regulated academic learning and identify the distinctive features of this capability for acquiring knowledge and skill. Drawing on subsequent articles in this journal issue as well as my research with colleagues, I discuss how the study of component processes contributes to our growing understanding of the distinctive features of students' self-regulated learning. Finally, the implications of self-regulated learning perspective on students' learning and achievement are considered.
A correlational study examined relationships between motivational orientation, self-regulated learning, and classroom academic performance for 173 seventh graders from eight science and seven English classes. A self-report measure of student self-efficacy, intrinsic value, test anxiety, self-regulation, and use of learning strategies was administered, and performance data were obtained from work on classroom assignments. Self-efficacy and intrinsic value were positively related to cognitive engagement and performance. Regression analyses revealed that, depending on the outcome measure, self-regulation, self-efficacy, and test anxiety emerged as the best predictors of performance. Intrinsic value did not have a direct influence on performance but was strongly related to self-regulation and cognitive strategy use, regardless of prior achievement level. The implications of individual differences in motivational orientation for cognitive engagement and self-regulation in the classroom are discussed. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Individuals with strong self-regulated learning (SRL) skills, characterized by the ability to plan, manage and control their learning process, can learn faster and achieve higher grades compared to those with weaker SRL skills. SRL is critical in learning environments that provide low levels of support and guidance, as is commonly the case in Massive Open Online Courses (MOOCs). Learners can be trained to engage in SRL and further supported by facilitating prompts, activities, and tools. However, effective implementation of learner support systems in MOOCs requires an understanding of which SRL strategies are most effective and how these strategies manifest in learner behavior. Moreover, identifying learner characteristics that are predictive of weaker SRL skills can advance efforts to provide targeted support without obtrusive survey instruments. We investigated SRL in a sample of 4831 learners across six MOOCs based on individual records of overall course achievement, interactions with course content, and survey responses. Results indicated that goal setting and strategic planning predicted attainment of personal course goals, while help seeking appeared to be counterproductive. Learners with stronger SRL skills were more likely to revisit previously studied course materials, especially course assessments. Several learner characteristics, including demographics and motivation, predicted learners’ SRL skills. We discuss implications and next steps towards online learning environments that provide targeted support and guidance.
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Publisher Summary
There is considerable agreement about the importance of self-regulation to human survival. There is disagreement about how it can be analyzed and defined in a scientifically useful way. A social cognitive perspective differs markedly from theoretical traditions that seek to define self-regulation as a singular internal state, trait, or stage that is genetically endowed or personally discovered. Instead, it is defined in terms of context-specific processes that are used cyclically to achieve personal goals. These processes entail more than metacognitive knowledge and skill; they also include affective and behavioral processes, and a resilient sense of self-efficacy to control them. The cyclical interdependence of these processes, reactions, and beliefs is described in terms of three sequential phases: forethought, performance or volitional control, and self-reflection. An important feature of this cyclical model is that it can explain dysfunctions in self-regulation, as well as exemplary achievements. Dysfunctions occur because of the unfortunate reliance on reactive methods of self-regulation instead of proactive methods, which can profoundly change the course of cyclical learning and performance. An essential issue confronting all theories of self-regulation is how this capability or capacity can be developed or optimized. Social cognitive views place particular emphasis on the role of socializing agents in the development of self-regulation, such as parents, teachers, coaches, and peers. At an early age, children become aware of the value of social modeling experiences, and they rely heavily on them when acquiring needed skills.
Building and using a personal learning environment is challenging and requires the students to have their metacognitive skills developed. Many students need help to develop these skills, to learn how to manage and evaluate their learning process in order to improve it. We designed an organizer with metacognitive support to help users to meet their learning goals. Here, we propose: 1) embedding learning strategies to support metacognitive learning; 2) a learning strategies recommender; 3) our vision of a learning analytics module for learners to support learning awareness and the reflection process; and 4) our solution for recommendations based on tag relationships, tag enrichment, and folksonomies.
One consequence of the pervasive use of computers is that most documents originate in digital form. Text mining the process of searching, retrieving, and analyzing unstructured, natural-language text is concerned with how to exploit the textual data embedded in these documents. Text Mining presents a comprehensive introduction and overview of the field, integrating related topics (such as artificial intelligence and knowledge discovery and data mining) and providing practical advice on how readers can use text-mining methods to analyze their own data. Emphasizing predictive methods, the book unifies all key areas in text mining: preprocessing, text categorization, information search and retrieval, clustering of documents, and information extraction. In addition, it identifies emerging directions for those looking to do research in the area. Some background in data mining is beneficial, but not essential. Topics and features: · Presents a comprehensive and easy-to-read introduction to text mining · Explores the application and utility of the methods, as well as the optimal techniques for specific scenarios · Provides several descriptive case studies that take readers from problem description to system deployment in the real world · Uses methods that rely on basic statistical techniques, thus allowing for relevance to all languages (not just English) · Includes access to downloadable software (runs on any computer), as well as useful chapter-ending historical and bibliographical remarks, a detailed bibliography, and subject and author indexes This authoritative and highly accessible text, written by a team of authorities on text mining, develops the foundation concepts, principles, and methods needed to expand beyond structured, numeric data to automated mining of text samples. Researchers, computer scientists, and advanced undergraduates and graduates with work and interests in data mining, machine learning, databases, and computational linguistics will find the work an essential resource. © 2005 Springer Science+Business Media. Inc. All rights reserved.
Proposal of a learning organization tool with support for metacognition
- M Manso-Vazquez
- M Llamas