Recommending Learning Materials Research Articles

Interactive experience of cross-cultural Japanese communication e-learning intelligent system based on computer information security

In the current situation of widespread digital media, e-learning has become the mainstream mode of online learning. Its intelligent and entertaining learning experience can enhance students' interest in learning. This article analyzes the interactive experience of cross-cultural Japanese communication e-learning intelligent system. The e-learning intelligent system can provide network platform support for the learning process, allowing students and teachers to engage in interactive learning through computer systems. At the same time, the system will also recommend learning materials that students are interested in. Adding some encryption techniques to the system in this article is mainly aimed at protecting the security of data. In order to build an intelligent communication system for cross-cultural Japanese, the main parameters of data information are generally used as the basis for experiments, and relevant coding principles are formulated. For some neural networks, the larger the stored values of the original training samples, the higher the sample size level of information parameters after successful decoding. The experimental results indicate that using data security encryption technology in computer information systems can effectively improve the overall performance of cross-cultural Japanese intelligent systems.

Developing a Learning Data Collection Platform for Learning Analytics in Online Education

During the COVID-19 pandemic, most education has been conducted through online classes. While feedback and interaction between students and instructors are significant in programming education or engineering practice, online education today cannot satisfy these aspects of learning. Therefore, this study proposes a learning support system for programming education and presents the results of designing and implementing this system. The proposed system consists of an online development environment module, a learning monitoring module, and a learning support module. It also provides a web-based programming environment, real-time chat and code mirroring, error guide messages and related lectures, e-learning quizzes, and learning activity analysis features. The system standardizes the development environment between the instructor and students, helps students take the initiative in solving errors, and enables code-oriented interactions between the instructor and students. It also collects data from all learning situations in the database. Conducting a big data analysis with the collected data will enable individual guidance for students by finding errors that frequently occur in programming and recommending learning materials to solve them.

Data analysis and personalized recommendation of western music history information using deep learning under Internet of Things.

To improve the teaching effect of western music history, the curriculum reform of history education needs to be promoted under the background of the Internet of Things (IoT). At first, a discussion is made on the characteristics of history course, which is combined with the characteristics of teaching data easy to collect under the background of IoT. An analysis is conducted on the related theory of educational data mining. Then, the concept of personalized recommendation is proposed based on deep learning (DL) algorithm. Finally, online and offline experiments are designed to verify the performance of the algorithm from review and investigation, smoothness, and participation of difficulty. The research results show that in terms of offline recommendation accuracy, the average record length in Math data set is 24.5, which is much smaller than that in range data set. The research has obvious innovation significance compared with other studies. In the process of target review and investigation, it is found that the research method here involves a wider range of knowledge and higher reliability. In terms of the difficulty of recommending questions, the Deep Reinforcement Exercise (DRE) recommendation algorithm can adaptively adjust the difficulty of recommending questions. It also allows students to set different learning goals through participation goals. But in the experiments on Math data set, Step 10’s recommendation results are not very good, and the difficulty level varies greatly. If the goal setting is high, the problem recommended to students is too difficult, students may answer these questions wrongly, forcing the algorithm to adjust the difficulty adaptively. According to the above results, DRE recommendation algorithm can adapt to different learning needs and customize the recommendation results, thus opening up a new path for the teaching of western music history. Besides, the combination of DL algorithm and western music history teaching design can recommend learning materials, which is of great significance in the teaching of history courses.

AI-Based Learning Style Prediction in Online Learning for Primary Education

Online learning has been widely applied due to developments in information technology. However, there are fewer relevant evaluations and applications for primary school students. All innovation efforts in learning are directed at improving the quality of education by creating an active learning atmosphere for students. Students’ participation in the teaching-learning process can be improved by selecting appropriate learning materials suitable to the student’s learning style. The research aims to develop and measure the impact of an Artificial-Intelligence (AI)-based learning style prediction model in an online learning portal for primary school students. The subjects were recruited from Indonesian primary school students in grades 4 to 6. To fulfill the principle of personalized learning, the AI model in the online learning portal was designed to recommend learning materials that suit students’ learning styles. We formulated a new AI approach that enables collaborative filtering-based AI models to be driven by learning style prediction. With this AI algorithm, the online learning portal can provide material recommendations tailored specifically to the learning style of each student. The AI model performance test achieved satisfactory results, with an average RMSE (Root Mean Squared Error) of 0.9035 from a rating scale of 1 to 5. Moreover, students’ learning performance was improved based on the results of t-test analysis on 269 subjects between the pre-test and post-test scores.

Toward a hybrid machine learning approach for extracting and clustering learners' behaviours in adaptive educational system

The learning style is a vital learner's characteristic that must be considered while recommending learning materials. In this paper we have proposed an approach to identify learning styles automatically. The first step of the proposed approach aims to preprocess the data extracted from the log file of the E-learning environment and capture the learners' sequences. The captured learners' sequences were given as an input to the K-means clustering algorithm to group them into sixteen clusters according to the FSLSM. Then the naive Bayes classifier was used to predict the learning style of a student in real time. To perform our approach, we used a real dataset extracted from an e-learning system's log file, and in order to evaluate the performance of the used classifier, the confusion matrix method was used. The obtained results demonstrate that our approach yields excellent results.

Toward a hybrid machine learning approach for extracting and clustering learners' behaviours in adaptive educational system

The learning style is a vital learner's characteristic that must be considered while recommending learning materials. In this paper we have proposed an approach to identify learning styles automatically. The first step of the proposed approach aims to preprocess the data extracted from the log file of the E-learning environment and capture the learners' sequences. The captured learners' sequences were given as an input to the K-means clustering algorithm to group them into sixteen clusters according to the FSLSM. Then the naive Bayes classifier was used to predict the learning style of a student in real time. To perform our approach, we used a real dataset extracted from an e-learning system's log file, and in order to evaluate the performance of the used classifier, the confusion matrix method was used. The obtained results demonstrate that our approach yields excellent results.

An Overview of Massive Open Online Course Platforms: Personalization and Semantic Web Technologies and Standards

Abstract Massive Open Online Course (MOOC) is a form of online education that provides great learning capabilities. Semantic Web technologies is an appropriate mechanism for personalization in MOOC platforms. The aim of this paper is to find out how Semantic Web is used to facilitate personalization in modern MOOC platforms. The paper describes state-of-the-art MOOC platforms from the position of personalization and Semantic Web features. We defined five personalization and five Semantic Web criteria as well as 20 MOOC platforms to review. The personalization criteria includes a personalized learning path, personalized navigation, recommendation system, personalized assessment, and personalized feedback. The Semantic Web criteria includes ontology, Resource Description Framework (RDF), Web Ontology Language (OWL), SPARQL Protocol and RDF Query Language (SPARQL), and Linked Data. The results show that most of the platforms support personalized feedback. Half of the platforms has personalized learning path tools. One third of the platforms allow personalized assessment. Three platforms recommend learning materials, and one platform allows personalized navigation. The selected platforms have poor Semantic Web technologies and standards support: three platforms use ontologies and none of the platforms supports other criteria: RDF, OWL, SPARQL, and Linked Data. Personalization tools are supported better than Semantic Web tools. Most of the platforms have no support for Semantic Web criteria. This means that currently Semantic Web is not used for personalization in the reviewed MOOC platforms.

Adaptive learning path recommender approach using auxiliary learning objects

In e-learning, one of the main difficulties is recommending learning materials that users can complete on time. It becomes more challenging when users cannot devote enough time to learn the entire course. In this paper, we describe two approaches to maximize users’ scores for a course while satisfying their time constraints. These approaches recommend successful paths based on the available time and knowledge background of users. We first briefly explain a method that has a similar goal to our method, and highlight its drawbacks. We then describe our proposal, which works based on a two-layered course graph (lesson and Learning Object (LO) layers; a lesson includes a few LO). Initially, our method uses the Depth First Search algorithm (DFS) to find all lesson sequences in the graph that start by a lesson (opted by a user). It then assigns LO for lessons of paths and estimates their score and time. Finally, a path that satisfies the user’s limited time while maximizing his/her score is recommended lesson by lesson. During a path recommendation, if the user could not get the estimated score from a lesson, our method recommends auxiliary LO for that lesson. To evaluate our method, we first assessed the quality of our estimation methods and then evaluate our recommender in a live environment. Results show that our estimation methods outperformed the ones in the literature. Results also present within the same amount of time, the users of our recommender proceeded more on the course than the users of another e-learning system.

Development Of The Blended Learning Model Of The Data System Of Data Basis In Faculty Of Computer Science, Banten Jaya University

This study aims to develop a database system product as a learning system teaching material with the application of e-learning. The method used in this study is the method of research and development (R & D). The development model used is the Dick and Carry Instructional Development Model. The steps for developing learning materials begin with needs analysis and formulation of objectives, instructional analysis, identifying early learner behaviors and characteristics, writing specific instructional objectives, writing benchmark reference tests, developing learning strategies, developing instructional materials, designing and implementing formative evaluations, revisions, obtaining desired instructional system. In general conclusions, validation from learning design experts 93%, material experts 95% and media experts 94% so recommending learning materials can be used. In individual trials 91.95%, small group trials 92.24% and in the 92.82% field trials, the database system teaching materials are feasible to be developed using e-learning models. Then the basic competencies to be achieved and indicators of the presentation of material on learning materials and e-learning programs in the database system subjects have been very good and easily understood by students.

On the way to learning style models integration: a Learner's Characteristics Ontology

On the way to increasing customization in e-learning systems, the learner model is the main source of variability. Such a model includes a number of psychological characteristics and study preferences that describe the learner's personality traits related to learning. During the last decades, the design methods and tools for e-learning have been designed assuming specific learner models. Therefore, in the search for a learning environment suitable for as many learner models as possible, we need tools to explore -and exploit- such models. In general, the learner's characteristics can be linked to the so-called learner's learning style (which is a part of the learner model) to provide the instructor with extensive knowledge about the learner's characterization in perceiving and processing information. Numerous learning styles have been proposed in the last decades, in some cases with overlapping characteristics with the same or different names. Thus, the heterogeneity of the learning style space makes it difficult to handle customization effectively. In this paper, we introduce a Learner's Characteristics Ontology based on creating interconnections between the different learning style model dimensions and learning styles with the relevant learner's characteristics, that: (1) helps instructors to improve and personalize the learning content; (2) can recommend learning materials to learners according to their learning characteristics and preferences; (3) can provide both instructors and learners with extensive knowledge about how they can improve their teaching and learning abilities; and (4) can improve communications and interaction between humans and computers by specifying the semantics of the learning style models' characteristics.

Prilagodljivi računalniški sistem za priporočanje učnih objektov v konstruktivističnem učnem okolju – ALECA

Dandanes se pojavlja vse več učnih sistemov, ki podpirajo aktivno učenje in upoštevajo učenčeve učne lastnosti, značilnosti in aktivnosti. V prispevku predstavljamo zasnovo učnega priporočilnega sistema, ki združuje znanja pedagogike in računalniških priporočilnih algoritmov. Proučujemo, kako združevanje modelov učnih stilov vpliva na izbiro različnih tipov večpredstavnih učnih gradiv. Rezultati kažejo, da študentje za učenje najpogosteje uporabljajo dobro strukturirana učna gradiva, ki vsebujejo barvno diskriminacijo, in da je hemisferični model učnih stilov najpomembnejši odločitveni kriterij. V nadaljevanju opisujemo postopek za reševanje t. i. problema hladnega zagona, s katerim je mogoče izboljšati točnost sistema za priporočanje učnih gradiv v okoljih, kjer o učencih nimamo predhodnih podatkov. Namen prispevka je predstaviti idejno zasnovo prilagodljivega učnega sistema z analizo njegovih predvidenih učinkov na učno prakso.

Providing Personalized Services to Users in a Recommender System

Instructors recommend learning materials to a class of students not minding the learning ability and reading habit of each student. Learners are finding it problematic to make a decision about which available learning materials best meet their situation and will be beneficial to their course of study. In order to address this challenge, a new e-learning material recommender system that is able to recommend quality items to learners individually is required. The aim of this work is to develop a Personalized Recommender System that switches between Content-based and Collaborative filtering techniques, with an objective to design an algorithm to recommend electronic library materials, as well as personalize recommendations to both new and existing users. Experiments were conducted with evaluations showing that the recommender system was most effective when content-based filtering and collaborative filtering were used to recommend items for new users and existing users respectively, and still achieve personalization.

PC Med Learner: a personalised and collaborative e-learning materials recommendation system using an ontology-based data matching strategy

It is important, in collaborative learning environments, to understand and assess the intrinsic knowledge of a learner and to share the knowledge within a learning community, in order to improve the learning process. This paper illustrates a framework and a method to recommend learning materials based on the learner's competencies and a domain ontology, in a collaborative setting. The approach is demonstrated in a learning scenario from medical organisations, when training their prentices. It aims at improving the learning processes by making personalised suggestions on the learning materials. The implementation of the system, the Personal and Collaborative Medical Learner (PC Med Learner) contains three main components: 1) a collaborative knowledge base; 2) an information visualisation tool; 3) an ontology-based data matching strategy, providing the evaluation methodology. Our approach can be adapted by corporate and educational organisations from various application domains, although we select the medical domain for the paper demonstration.

Evaluating collaborative filtering recommendations inside large learning object repositories

Collaborative filtering (CF) algorithms are techniques used by recommender systems to predict the utility of items for users based on the similarity among their preferences and the preferences of other users. The enormous growth of learning objects on the internet and the availability of preferences of usage by the community of users in the existing learning object repositories (LORs) have opened the possibility of testing the efficiency of CF algorithms on recommending learning materials to the users of these communities. In this paper we evaluated recommendations of learning resources generated by different well known memory-based CF algorithms using two databases (with implicit and explicit ratings) gathered from the popular MERLOT repository. We have also contrasted the results of the generated recommendations with several existing endorsement mechanisms of the repository to explore possible relations among them. Finally, the recommendations generated by the different algorithms were compared in order to evaluate whether or not they were overlapping. The results found here can be used as a starting point for future studies that account for the specific context of learning object repositories and the different aspects of preference in learning resource selection.

Integrated GPS and QRCode in Personalized Mobile Learning System Based on Rocommendation Mechanism

With the fast development of personal communication network and mobile technology, various mobile communication applications have already been incorporated into our life. Therefore, the mobile learning is becoming a hot issue too. This paper combines the mobile network with the GPS technology to use the characteristic of fast input of QR-Code technology taking action in study, and utilize the data mining technique which extracts from the learner’s e-portfolio data to achieve the learners to enjoy and get the suitable and individualized study teaching material whenever and wherever possible. And we will focus on how to fully develop the fast and large memory ability of the QR-code to combine the analysis of the learner’s learning records into the m-learning to provide the real-time recommending learning materials. Finally, through some experiments about some different QR-code input efficient and correct we found the certainly and greatly improved the learning results through our personalized mobile learning system.

Measuring learner's performance in e-learning recommender systems

<span>A recommender system is a piece of software that helps users to identify the most interesting and relevant learning items from a large number of items. Recommender systems may be based on collaborative filtering (by user ratings), content-based filtering (by keywords), and hybrid filtering (by both collaborative and content-based filtering). Recommender systems have been a useful tool to recommend items in many online systems, including e-learning. However, not much research has been done to measure the learning outcomes of the learners when they use e-learning with a recommender system. Instead, most of the researchers were focusing on the accuracy of the recommender system in predicting the recommendation rather than the knowledge gain by the learners. This research aims to compare the learning outcomes of the learners when they use several types of e-learning recommender systems. Based on the comparison made, we propose a new e-learning recommender system framework that uses content-based filtering and good learners' ratings to recommend learning materials, and in turn is able to increase the student's performance. The results show that students who used the proposed e-learning recommender system produced a significantly better result in the post-test. The results also show that the proposed e-learning recommender system has the highest percentage of score gain from pre-test to post-test.</span>

Learning materials recommendation using good learners’ ratings and content-based filtering

The enormity of the amount of learning materials in e-learning has led to the difficulty of locating suitable learning materials for a particular learning topic, creating the need for recommendation tools within a learning context. In this paper, we aim to address this need by proposing a novel e-learning recommender system framework that is based on two conceptual foundations—peer learning and social learning theories that encourage students to cooperate and learn among themselves. Our proposed framework works on the idea of recommending learning materials with a similar content and indicating the quality of learning materials based on good learners’ ratings. A comprehensive set of experiments were conducted to measure the system accuracy and its impact on learner’s performance. The obtained results show that the proposed e-learning recommender system has a significant improvement in the post-test of about 12.16% with the effect size of 0.6 and 13.11% with the effect size of 0.53 when compared to the e-learning with a content-based recommender system and the e-learning without a recommender system, respectively. Furthermore, the proposed recommender system performed better in terms of having a small rating deviation and a higher precision as compared to e-learning with a content-based recommender system.