Template-driven teacher modelling approach : a thesis submitted in partial fulfilment of the requirements for the degree of Master of Science in Information Science at Massey University, Palmerston North

This thesis describes the Template-driven Teacher Modeling Approach, the initial implementation of the template server and the formative evaluation on the prototype. The initiative of Template-driven teacher modeling is to integrate the template server and intelligent teacher models in Web-based education systems for course authoring. There are a number of key components in the proposed system: user interface, template server and content repository. The Template-Driven Teacher Modeling (TDTM) architecture supports the course authoring by providing higher degree of control over the generation of presentation. The collection of accumulated templates in the template repository for a teacher or a group of teachers are selected as the inputs for the inference mechanism in teacher's model to calculate the best representation of the teaching strategy, and then predict teacher intention when he or she interacts with the system. Moreover, the presentation templates are kept to support the re-use of the on-line content at the level of individual screens with the help of Template Server

Designing web-based adaptive learning environment : distils as an example

In this study, two components are developed for the Web-based adaptive learning: an online Intelligent Tutoring Tool (ITT) and an Adaptive Lecture Guidance (ALG). The ITT provides students timely problem-solving help in a dynamic Web environment. The ALG prevents students from being disoriented when a new domain is presented using Web technology. A prototype, Distributed Intelligent Learning System (DISTILS), has been implemented in a general chemistry laboratory domain. In DISTILS, students interact with the ITT through a Web browser. When a student selects a problem, the problem is formatted and displayed in the user interface for the student to solve. On the other side, the ITT begins to solve the problem simultaneously. The student can then request help from the ITT through the interface. The ITT interacts with the student, verifying those solution activities in an ascending order of the student knowledge status. In DISTILS, a Web page is associated with a HTML Learning Model (HLM) to describe its knowledge content. The ALG extracts the HLM, collects the status of students\u27 knowledge in HLM, and presents a knowledge map illustrating where the student is, how much proficiency he/she already has and where he/she is encouraged to explore. In this way, the ALG helps students to navigate the Web-based course material, protecting them from being disoriented and giving them guidance in need. Both the ITT and ALG components are developed under a generic Common Object Request Broker Architecture (CORBA)-driven framework. Under this framework, knowledge objects model domain expertise, a student modeler assesses student\u27s knowledge progress, an instruction engine includes two tutoring components, such as the ITT and the ALG, and the CORBA-compatible middleware serves as the communication infrastructure. The advantage of such a framework is that it promotes the development of modular and reusable intelligent educational objects. In DISTILS, a collection of knowledge objects were developed under CORBA to model general chemistry laboratory domain expertise. It was shown that these objects can be easily assembled in a plug-and-play manner to produce several exercises for different laboratory experiments. Given the platform independence of CORBA, tutoring objects developed under such a framework have the potential to be easily reused in different applications. Preliminary results showed that DISTILS effectively enhanced learning in Web environment. Three high school students and twenty-two NJIT students participated in the evaluation of DISTILS. In the final quiz of seven questions, the average correct answers of the students who studied in a Web environment with DISTILS (DISTILS Group) was 5.3, and the average correct answers of those who studied in the same Web environment without DISTILS (NoDISTILS Group) was 2.75. A t-test conducted on this small sample showed that the DISTILS group students significantly scored better than the NoDISTILS group students

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

A gentle transition from Java programming to Web Services using XML-RPC

Exposing students to leading edge vocational areas of relevance such as Web Services can be difficult. We show a lightweight approach by embedding a key component of Web Services within a Level 3 BSc module in Distributed Computing. We present a ready to use collection of lecture slides and student activities based on XML-RPC. In addition we show that this material addresses the central topics in the context of web services as identified by Draganova (2003)

Graduate Catalog, 2005-2006

https://scholar.valpo.edu/gradcatalogs/1032/thumbnail.jp

Prediction of student success: A smart data-driven approach

Predicting student’s academic performance is one of the subjects related to the Educational Data Mining process, which intends to extract useful information and new patterns from educational data. Understanding the drivers of student success may assist educators in developing pedagogical methods providing a tool for personalized feedback and advice. In order to improve the academic performance of students and create a decision support solution for higher education institutes, this dissertation proposed a methodology that uses educational data mining to compare prediction models for the students' success. Data belongs to ISCTE master students, a Portuguese university, during 2012 to 2022 academic years. In addition, it was studied which factors are the strongest predictors of the student’s success. PyCaret library was used to compare the performance of several algorithms. Factors that were proposed to influence the success include, for example, the student's gender, previous educational background, the existence of a special statute, and the parents' educational degree. The analysis revealed that the Light Gradient Boosting Machine Classifier had the best performance with an accuracy of 87.37%, followed by Gradient Boosting Classifier (accuracy = 85.11%) and Adaptive Boosting Classifier (accuracy = 83.37%). Hyperparameter tunning improved the performance of all the algorithms. Feature importance analysis revealed that the factors that impacted the student’s success most were the average grade, master time, and the gap between degrees, i.e., the number of years between the last degree and the start of the master.A previsão do sucesso académico de estudantes é um dos tópicos relacionados com a mineração de dados educacionais, a qual pretende extrair informação útil e encontrar padrões a partir de dados académicos. Compreender que fatores afetam o sucesso dos estudantes pode ajudar, as instituições de educação, no desenvolvimento de métodos pedagógicos, dando uma ferramenta de feedback e aconselhamento personalizado. Com o fim de melhorar o desempenho académico dos estudantes e criar uma solução de apoio à decisão, para instituições de ensino superior, este artigo propõe uma metodologia que usa mineração de dados para comparar modelos de previsão para o sucesso dos alunos. Os dados pertencem a alunos de mestrado que frequentaram o ISCTE, uma universidade portuguesa, durante os anos letivos de 2012 a 2022. Além disso, foram estudados quais os fatores que mais afetam o sucesso do aluno. Os vários algoritmos foram comparados pela biblioteca PyCaret. Alguns dos fatores que foram propostos como relevantes para o sucesso incluem, o género do aluno, a formação educacional anterior, a existência de um estatuto especial e o grau de escolaridade dos pais. A análise dos resultados demonstrou que o classificador Light Gradient Boosting Machine (LGBMC) é o que tem o melhor desempenho com uma accuracy de 87.37%, seguindo-se o classificador Gradient Boosting Classifier (accuracy=85.11%) e o classificador Adaptive Boosting (accuracy=83.37%). A afinação de hiperparâmetros melhorou o desempenho de todos os algoritmos. As variáveis que demonstraram ter maior impacto foram a média dos estudantes, a duração do mestrado e o intervalo entre estudos

A course-oriented intelligent tutoring system with probability assessment

Most Intelligent Tutoring Systems (ITSs) in the past have concentrated on small domains and have been topic-oriented. They have tended to be non-extendable prototypes and have neglected the expertise of human teachers. It is argued here that a promising approach at this time is to design course-oriented ITS shells which are based on the human teacher. Courses using such shells could be used to take some of the load of first-time delivery and assessment from teachers and lecturers, and leave them more time for individual tutoring. [Continues.