Mining Web-based Educational Systems to Predict Student Learning Achievements

Educational Data Mining (EDM) is getting great importance as a new interdisciplinary research field related to some other areas. It is directly connected with Web-based Educational Systems (WBES) and Data Mining (DM, a fundamental part of Knowledge Discovery in Databases). The former defines the context: WBES store and manage huge amounts of data. Such data are increasingly growing and they contain hidden knowledge that could be very useful to the users (both teachers and students). It is desirable to identify such knowledge in the form of models, patterns or any other representation schema that allows a better exploitation of the system. The latter reveals itself as the tool to achieve such discovering. Data mining must afford very complex and different situations to reach quality solutions. Therefore, data mining is a research field where many advances are being done to accommodate and solve emerging problems. For this purpose, many techniques are usually considered. In this paper we study how data mining can be used to induce student models from the data acquired by a specific Web-based tool for adaptive testing, called SIETTE. Concretely we have used top down induction decision trees algorithms to extract the patterns because these models, decision trees, are easily understandable. In addition, the conducted validation processes have assured high quality models

A conceptual analytics model for an outcome-driven quality management framework as part of professional healthcare education

BACKGROUND: Preparing the future health care professional workforce in a changing world is a significant undertaking. Educators and other decision makers look to evidence-based knowledge to improve quality of education. Analytics, the use of data to generate insights and support decisions, have been applied successfully across numerous application domains. Health care professional education is one area where great potential is yet to be realized. Previous research of Academic and Learning analytics has mainly focused on technical issues. The focus of this study relates to its practical implementation in the setting of health care education. OBJECTIVE: The aim of this study is to create a conceptual model for a deeper understanding of the synthesizing process, and transforming data into information to support educators’ decision making. METHODS: A deductive case study approach was applied to develop the conceptual model. RESULTS: The analytics loop works both in theory and in practice. The conceptual model encompasses the underlying data, the quality indicators, and decision support for educators. CONCLUSIONS: The model illustrates how a theory can be applied to a traditional data-driven analytics approach, and alongside the context- or need-driven analytics approach

Exploring the Effectiveness of AI Algorithms in Predicting and Enhancing Student Engagement in an E-Learning

The shift from traditional to digital learning platforms has highlighted the need for more personalized and engaging student experiences. In response, researchers are investigating AI algorithms' ability to predict and improve e-learning student engagement.  Machine Learning (ML) methods like Decision Trees, Support Vector Machines, and Deep Learning models can predict student engagement using variables like interaction patterns, learning behavior, and academic performance. These AI algorithms have identified at-risk students, enabling early interventions and personalized learning. By providing adaptive content, personalized feedback, and immersive learning environments, some AI methods have increased student engagement. Despite these advances, data privacy, unstructured data, and transparent and interpretable models remain challenges. The review concludes that AI has great potential to improve e-learning outcomes, but these challenges must be addressed for ethical and effective applications. Future research should develop more robust and interpretable AI models, multidimensional engagement metrics, and more comprehensive studies on AI's ethical implications in education

E-Learner Recommendation Model Based on Level of Learning Outcomes Achievement

Students in any learning environment differ in their level of knowledge, achieved learning outcomes, learning style, preferences, misunderstand and attempts in solving and addressing problems when their expectations are not met. When a student searches the web as an attempt to solve a problem, he suffers from the large number of resources which are, in most cases, not related to his “needs”, or may be related but complex and advance. The result of his search might make him more confused, scattered, depressed and finally result in wasting his time which – in some cases -may have negative effects on his achievements. From here comes the need for an intelligent learning system that can guide studentsbased on their needs. This research attempts to design and build an educational recommender system for a web-based learning environment in order to generate meaningful recommendations of the most interested and relevant learning materials that suit students’ needs based on their profiles1 . This can be achieved by accessing students’ history, exploring their learning navigation patterns and making use of similar students’ experiences and their success stories. The study proposed a design for a hybrid recommender system architecture which consists of two recommendation approaches: the content and collaborative filtering. The study concentrates on the collaborative recommender engine which will recommend learning materials based on students’ level of knowledge, looking at active students' profiles, and achievements in both learning outcomes and learning outcomes levels making use of similar students’ success stories and reflecting their good experience on active student who are in the same level of knowledge. The design of the collaborative recommender engine includes the “learning” module from which the engine learns past students’ access pattern and the “advising” module from which the engine reflects the experience of similar success stories on active students. The content base recommender engine with its suggested stages is considered as future work, the research used the k-mean cluster algorithm to find out similar students where five distance function are used: Euclidean, Correlation. Jaccard,cosine and Manhattan. The cosine function shows to be the most accurate distance function with the minimum SSE but the highest processing time that doesn’t differ a lot when compared the rest functions. The best number of clusters for the selected dataset was determined using three methods Elbow, Gap-statistic and average Silhouette approach where the best number of cluster shows to be three. The research used the two result rating matrices of similar good and good students with Learnings material in order to calculate learning material weights and rank them based on highest weights which results in a final recommendation list

RiPLE: Recommendation in Peer-Learning Environments Based on Knowledge Gaps and Interests

Various forms of Peer-Learning Environments are increasingly being used in post-secondary education, often to help build repositories of student generated learning objects. However, large classes can result in an extensive repository, which can make it more challenging for students to search for suitable objects that both reflect their interests and address their knowledge gaps. Recommender Systems for Technology Enhanced Learning (RecSysTEL) offer a potential solution to this problem by providing sophisticated filtering techniques to help students to find the resources that they need in a timely manner. Here, a new RecSysTEL for Recommendation in Peer-Learning Environments (RiPLE) is presented. The approach uses a collaborative filtering algorithm based upon matrix factorization to create personalized recommendations for individual students that address their interests and their current knowledge gaps. The approach is validated using both synthetic and real data sets. The results are promising, indicating RiPLE is able to provide sensible personalized recommendations for both regular and cold-start users under reasonable assumptions about parameters and user behavior.Comment: 25 pages, 7 figures. The paper is accepted for publication in the Journal of Educational Data Minin

A LEARNER INTERACTION STUDY OF DIFFERENT ACHIEVEMENT GROUPS IN MPOCS WITH LEARNING ANALYTICS TECHNIQUES

The purpose of this study was to conduct data-driven research by employing learning analytics methodology and Big Data in learning management systems (LMSs), and then to identify and compare learners’ interaction patterns in different achievement groups through different course processes in Massive Private Online Courses (MPOCs). Learner interaction is the foundation of a successful online learning experience. However, the uncertainties about the temporal and sequential patterns of online interaction and the lack of knowledge about using dynamic interaction traces in LMSs have prevented research on ways to improve interactive qualities and learning effectiveness in online learning. Also, most research focuses on the most popular online learning organization form, Massive Open Online Courses (MOOCs), and little online learning research has been conducted to investigate learners’ interaction behaviors in another important online learning organization form: MPOCs. To fill these needs, the study pays attention to investigate the frequent and effective interaction patterns in different achievement groups as well as in different course processes, and attaches importance to LMS trace data (log data) in better serving learners and instructors in online learning. Further, the learning analytics methodology and techniques are introduced here into online interaction research. I assume that learners with different achievements express different interaction characteristics. Therefore, the hypotheses in this study are: 1) the interaction activity patterns of the high-achievement group and the low-achievement group are different; 2) in both groups, interaction activity patterns evolve through different course processes (such as the learning process and the exam process). The final purpose is to find interaction activity patterns that characterize the different achievement groups in specific MPOCs courses. Some learning analytics approaches, including Hidden Markov models (HMMs) and other related measures, are taken into account to identify frequently occurring interaction activity sequence patterns of High/Low achievement groups in the Learning/Exam processes under MPOCs settings. The results demonstrate that High-achievement learners especially focused on content learning, assignments, and quizzes to consolidate their knowledge construction in both Learning and Exam processes, while Low-achievement learners significantly did not perform the same. Further, High-achievement learners adjusted their learning strategies based on the goals of different course processes; Low-achievement learners were inactive in the learning process and opportunistic in the exam process. In addition, despite achievements or course processes, all learners were most interested in checking their performance statements, but they engaged little in forum discussion and group learning. In sum, the comparative analysis implies that certain interaction patterns may distinguish the High-achievement learners from the Low-achievement ones, and learners change their patterns more or less based on different course processes. This study provides an attempt to conduct learner interaction research by employing learning analytics techniques. In the short term, the results will give in-depth knowledge of the dynamic interaction patterns of MPOCs learners. In the long term, the results will help learners to gain insight into and evaluate their learning, help instructors identify at-risk learners and adjust instructional strategies, help developers and administrators to build recommendation systems based on objective and comprehensive information, all of which in turn will help to improve the achievements of all learner groups in specific MPOC courses