ALBERT-Based Personalized Educational Recommender System: Enhancing Students’ Learning Outcomes in Online Learning

Online learners must navigate vast educational resources to find materials that meet their needs. This study introduces an ALBERT-based personalized educational recommender system to improve student learning. ALBERT (A Lite BERT), an optimized variant of the BERT algorithm, captures contextualized word representations and understands the semantic meaning of learning resources, student profiles, and interactions. This study evaluates the ALBERT-based recommender system’s personalized learning recommendations. To assess learning outcomes, a diverse group of students from different educational domains is evaluated. Before and after the recommender system, academic performance, knowledge retention, and engagement are assessed. User satisfaction surveys assess recommendation quality, relevance, and user experience. The recommender system uses ALBERT’s model optimization to improve recommendation accuracy, learner engagement, and personalized learning. The evaluation shows the ALBERT-based personalized recommender system improves online learning outcomes. System-generated recommendations boost student engagement, knowledge retention, and academic performance. User satisfaction surveys show that the ALBERT-based system meets learners’ needs by providing relevant and high-quality recommendations. This research shows how advanced deep learning algorithms like ALBERT can improve personalized online learning. ALBERT’s optimized training and inference speeds up the recommender system’s scalability. This empowers learners to access tailored and high-quality educational resources, maximizing their learning outcomes and potential in online learning

Attention-based High-order Feature Interactions to Enhance the Recommender System for Web-based Knowledge-Sharing Servic

Providing personalized online learning services has become a hot research topic. Online knowledge-sharing services represents a popular approach to enable learners to use fragmented spare time. User asks and answers questions in the platform, and the platform also recommends relevant questions to users based on their learning interested and context. However, in the big data era, information overload is a challenge, as both online learners and learning resources are embedded in data rich environment. Offering such web services requires an intelligent recommender system to automatically filter out irrelevant information, mine underling user preference, and distil latent information. Such a recommender system needs to be able to mine complex latent information, distinguish differences between users efficiently. In this study, we refine a recommender system of a prior work for web-based knowledge sharing. The system utilizes attention-based mechanisms and involves high-order feature interactions. Our experimental results show that the system outperforms known benchmarks and has great potential to be used for the web-based learning service

Proceedings of the 3rd Workshop on Social Information Retrieval for Technology-Enhanced Learning

Learning and teaching resource are available on the Web - both in terms of digital learning content and people resources (e.g. other learners, experts, tutors). They can be used to facilitate teaching and learning tasks. The remaining challenge is to develop, deploy and evaluate Social information retrieval (SIR) methods, techniques and systems that provide learners and teachers with guidance in potentially overwhelming variety of choices. The aim of the SIRTEL’09 workshop is to look onward beyond recent achievements to discuss specific topics, emerging research issues, new trends and endeavors in SIR for TEL. The workshop will bring together researchers and practitioners to present, and more importantly, to discuss the current status of research in SIR and TEL and its implications for science and teaching

Some theoretical and practical aspects of educational portal design based on CMS System

In this paper the authors describe and review their own experience as well as the experience of other countries in the use of of CMS for the development of educational portals. In addition, the paper includes a preview of system side requirements for such CMS as Joomla!, Drupal, WordPress, PHP-Fusion, Web@all CMS, Mambo and eXtreme-Fusion. The authors propose a concept of an educational portal that meets the needs of students, academic teachers, lecturers and administrative staff of the university. A description of the layers and target groups of this educational portal is provided, too. Moreover, the article examines results of a student survey relating to their expectations concerning the faculty’s educational portal. The last section of the article provides advice for designers of CMS-based portals, based on the authors' many years of experience in the field and an analysis of the available examples.Uniwersytet Śląsk

Retail Store Ranking Recommender System

Companies have been changing and developing their marketing tools and techniques in order to follow the rapid growth of e-commerce in many aspects, and they specifically try to target customers by offering them the products and services they need using recommender engines. Moreover, the rapid growth in e-commerce resulted in people placing the web as the source of information to buy or sell from. Therefore, other than normal stores, many online shops exist, in different forms, from websites with private domain to thread in online forums. This has advantages which is that people have more options to shop from, but at the same time it is also a disadvantage where with so many options, customers will find difficulties to choose which store is more suitable to buy a product from. In this capstone project we study the time consumed by the customer to find a suitable website to buy a desired device using a recommender data analytics approach. The purpose of the project is to build a recommender system that recommends a store to buy a product from based on the user entry parameters. As well as to help the stores to increase their ranking in the recommender engine by using analytical models. Data will be extracted from electronic stores in the UAE. Data was visualized, preprocessed, and suitable attributes were chosen before building the models

Pairing-based approach to support understanding of object-oriented concepts and programming

Blended learning comprises various learning modes with the support of digital resources. It has been a critical element in 21st-century teaching and learning environment at multiple levels of education, mainly tertiary level. In the software engineering field, pair programming is one of the techniques in Xtreme Programming principles in Agile software development methodology. Although pair programming is well-known among practitioners, studies have shown that pair programming can support computer science or software engineering students at higher learning institutions to understand the concepts in programming. Indeed, pair programming could support active learning among students. Inspired by pair programming, this study proposes that pairing-based pedagogy or "pairgogy" in blended learning could also increase students' confidence and interest in completing theoretical in-class exercises not limited to programming tasks with the support of an e-learning system. The proposed approach was applied to teaching object-oriented concepts using Java programs. The findings reflect that both pair programming and "pairgogy" complement each other as a pairing-based approach in blended learning to support understanding of object-oriented concepts and programming. Students' responses towards the approach applied in a semester were positive. The study also implies that most students preferred to be a driver, the person doing the program rather than a navigator who guides drivers on what to program. In this approach, students were also required to complete the in-class theoretical questions in pairs by tracing given programs and answered via the e-learning. Thus, the pairing-based approach has proven to be beneficial to support students in learning programming

A review of the Development Trend of Personalized learning Technologies and its Applications

Personalized learning tailors material and strategy to student requirements, interests, and goals in e-learning. These developments help educational institutions and other organizations to keep up with the fast pace of information technology, communications, and computing power. Studies show that self-adaptive learning and relevant learning information improve study efficiency. Compared to traditional teaching methods, the practice of online education is well in its infancy. On the other hand, the pedagogy and evaluation of students in online courses have a large gap that has to be filled, necessitating significant improvements in e-learning. We call this approach to education "personalized learning," which is a central focus of today's leading online education platforms. Several studies have been conducted on e-learning and personalized learning, but few investigated the development trend of personalized learning technologies and applications. Therefore this study examines the literature to close the gap and promote the development trend for personalized learning technologies and applications in higher education from 2010 to 2021 by analyzing related journal articles. The pivotal studies used inclusion criteria after a search generated 372 complete research articles and reduced them to 146 publications based on their proposed learning domains and research themes. Through carefully reviewing current trends and successes in numerous aspects of personalized learning, this discussion analyzes prospective future research directions in the field of personalized learning

Pairing-Based Approach to Support Understanding of Object-Oriented Concepts and Programming

Blended learning comprises various learning modes with the support of digital resources. It has been a critical element in 21st-century teaching and learning environment at multiple levels of education, mainly tertiary level. In the software engineering field, pair programming is one of the techniques in Xtreme Programming principles in Agile software development methodology. Although pair programming is well-known among practitioners, studies have shown that pair programming can support computer science or software engineering students at higher learning institutions to understand the concepts in programming. Indeed, pair programming could support active learning among students. Inspired by pair programming, this study proposes that pairing-based pedagogy or “pairgogy” in blended learning could also increase students’ confidence and interest in completing theoretical in-class exercises not limited to programming tasks with the support of an e-learning system. The proposed approach was applied to teaching object-oriented concepts using Java programs. The findings reflect that both pair programming and “pairgogy” complement each other as a pairing-based approach in blended learning to support understanding of object-oriented concepts and programming. Students’ responses towards the approach applied in a semester were positive. The study also implies that most students preferred to be a driver, the person doing the program rather than a navigator who guides drivers on what to program. In this approach, students were also required to complete the in-class theoretical questions in pairs by tracing given programs and answered via the e-learning. Thus, the pairing-based approach has proven to be beneficial to support students in learning programming

Aprendizaje móvil: perspectivas

El futur de l’aprenentatge, des d’una perspectiva tècnica, està integrat per quatre eixos que el defineixen i sobre els quals s’articulen esforços tecnològics i metodològics. Aquests eixos són: la mobilitat, la interacció, la intel·ligència artificial i recursos basats en tecnologia com la realitat augmentada i els jocs aplicats a l’aprenentatge. La seva combinació suposa la creació d’un model d’escenaris mòbils, interactius i intel·ligents que aprofiten tots els espais i temps disponibles per a l’aprenent. Les diferents tecnologies, cadascuna per la seva banda, ja estan disponibles i són utilitzades en diverses experiències educatives; el que cal és la conjugació d’aquestes experiències a través de models didàctics en els quals l’aprenentatge assolit pels estudiants sigui significatiu. En aquest article es discuteixen aquestes tecnologies i es planteja un model d’integració que possibilita l’establiment d’un marc referencial de treball didàctic. Es conclou la necessitat d’experimentar tecnologies i plasmar-ne els resultats en models d’ensenyament-aprenentatge que utilitzin esquemes d’interacció alternatius i la urgència de disposar de sistemes tutorials intel·ligents per a massificar la tutoria.From a technical perspective, the future of learning is defined by four axes around which technological and methodological efforts revolve. These axes are mobility, interaction, artificial intelligence and technology-based resources such as augmented reality and games applied to learning. Combining them means creating a model of mobile, interactive and intelligent scenarios that take advantage of the spaces and times available to the learner. The various technologies are already available yet used separately in different educational experiences. It is therefore crucial to combine and integrate them into didactic models wherein the learning attained by students is significant. This article discusses these technologies and proposes an integrative model that enables a framework of reference for didactic work to be established. It concludes by highlighting the need to experiment with technologies and to apply the results to teaching-learning models using alternative interaction schema, and the urgency of having intelligent tutoring systems to make tutoring available on a massive scale.El futuro del aprendizaje, desde una perspectiva técnica, está integrado por cuatro ejes que lo definen y sobre los que se articulan esfuerzos tecnológicos y metodológicos. Estos ejes son: la movilidad, la interacción, la inteligencia artificial y recursos basados en tecnología como la realidad aumentada y los juegos aplicados al aprendizaje. Su combinación supone la creación de un modelo de escenarios móviles, interactivos e inteligentes que aprovechan todos los espacios y tiempos disponibles para el aprendiente. Las distintas tecnologías, cada una por su lado, ya están disponibles y son utilizadas en diversas experiencias educativas; lo que se hace necesario es la conjugación de estas a través de modelos didácticos en los que el aprendizaje alcanzado por los estudiantes sea significativo. En este artículo se discuten estas tecnologías y se plantea un modelo de integración que posibilita el establecimiento de un marco referencial de trabajo didáctico. Se concluye la necesidad de experimentar tecnologías y plasmar los resultados en modelos de enseñanza-aprendizaje que utilicen esquemas de interacción alternativos y la urgencia de contar con sistemas tutoriales inteligentes para masificar la tutoría