Adaptive formative assessment system based on computerized adaptive testing and the learning memory cycle for personalized learning

Computerized adaptive testing (CAT) can effectively facilitate student assessment by dynamically selecting questions on the basis of learner knowledge and item difficulty. However, most CAT models are designed for one-time evaluation rather than improving learning through formative assessment. Since students cannot remember everything, encouraging them to repeatedly evaluate their knowledge state and identify their weaknesses is critical when developing an adaptive formative assessment system in real educational contexts. This study aims to achieve this goal by proposing an adaptive formative assessment system based on CAT and the learning memory cycle to enable the repeated evaluation of students' knowledge. The CAT model measures student knowledge and item difficulty, and the learning memory cycle component of the system accounts for students’ retention of information learned from each item. The proposed system was compared with an adaptive assessment system based on CAT only and a traditional nonadaptive assessment system. A 7-week experiment was conducted among students in a university programming course. The experimental results indicated that the students who used the proposed assessment system outperformed the students who used the other two systems in terms of learning performance and engagement in practice tests and reading materials. The present study provides insights for researchers who wish to develop formative assessment systems that can adaptively generate practice tests

DEVELOPMENT OF AN ONTOLOGY-BASED ADAPTIVE PERSONALIZED E-LEARNING SYSTEM

E-learning has fast become an active field of research with a lot of investments towards web-based delivery of personalized learning contents to learners. Some issues of e-learning arise from the heterogeneity and interoperability of learning content adapting to learner's styles and preferences. This has brought about the development of an ontology-based personalized learning system to solve this problem. This research developed an ontology-based personalized e-learning system that presents suitable learning contents to learners based on their learning style, preferences, background knowledge, and personal profile.&nbsp

An adaptive mobile learning system for learning a new language based on learner’s abilities

The rapid development of wireless infrastructure and wide use of mobile devices in our daily life has a major impact on our way of learning using computing technology. Particularly, learning a new language is a challenging concept for researcher. Furthermore, adaptive services is nowadays an important research topic in the field of web-based and mobile learning systems as there are no fixed learning path which are appropriate for all learners. However, most studies in this field have only focus on learning style and habits of learners. Far too little attention has been paid on the ability of learner. Therefore, the purpose of this paper is to propose a new adaptive mobile learning model for learning new languages based on ability of learner. Furthermore, an ontology-based knowledge modelling technique is proposed to classify language learning materials and describe user profile in order to provide adaptive learning environment

Personalised mobile learning system based on item response theory

Rapid advancements in the design and integration of mobile devices and networked technologies in day to day activities are creating new perceptions about the exploitation of mobile technologies in teaching and learning. Consequently, there is growing demand for personalised, efficient and flexible systems for supporting learning in various settings. However, fulfilling learner demand for personalised support requires better understanding of activities, operational contexts and purposes for which mobile devices are deployed to support learning. Therefore, our position with regards to methods for researching mobile learning focuses on personalised learning. This paper presents an approach to designing a personalised learning system by analysing the ability of the learner based on Items Response Theory. Furthermore, in the proposed system user profile is modelled based on profile ontology

A personalized adaptive e-learning approach based on semantic web technology

Recent developments in semantic web technologies heightened the need for online adaptive learning environment. Adaptive learning is an important research topic in the field of web-based systems as there are no fixed learning paths which are appropriate for all learners. However, most studies in this field have only focused on learning styles and habits of learners. Far too little attention has been paid on understanding the ability of learners. Therefore, it is becoming increasingly difficult to ignore adaptation in the field of e-learning systems. Many researchers are adopting semantic web technologies to find new ways for designing adaptive learning systems based on describing knowledge using ontological models. Ontologies have the potential to design content and learner models required to create adaptive e-learning systems based on various characteristics of learners. The aim of this paper is to present an ontology-based approach to develop adaptive e-learning system based on the design of semantic content, learner and domain models to tailor the teaching process for individual learner’s needs. The proposed new adaptive e-learning has the ability to support personalization based on learner’s ability, learning style, preferences and levels of knowledge. In our approach the ontological user profile is updated based on achieved learner’s abilities

Survey of Personalized Learning Software Systems: A Taxonomy of Environments, Learning Content, and User Models

This paper presents a comprehensive systematic review of personalized learning software systems. All the systems under review are designed to aid educational stakeholders by personalizing one or more facets of the learning process. This is achieved by exploring and analyzing the common architectural attributes among personalized learning software systems. A literature-driven taxonomy is recognized and built to categorize and analyze the reviewed literature. Relevant papers are filtered to produce a final set of full systems to be reviewed and analyzed. In this meta-review, a set of 72 selected personalized learning software systems have been reviewed and categorized based on the proposed personalized learning taxonomy. The proposed taxonomy outlines the three main architectural components of any personalized learning software system: learning environment, learner model, and content. It further defines the different realizations and attributions of each component. Surveyed systems have been analyzed under the proposed taxonomy according to their architectural components, usage, strengths, and weaknesses. Then, the role of these systems in the development of the field of personalized learning systems is discussed. This review sheds light on the field’s current challenges that need to be resolved in the upcoming years

Adaptive e-learning system using ontology

This paper proposes an innovative ontological approach to design a personalised e-learning system which creates a tailored workflow for individual learner. Moreover, the learning content and sequencing logic is separated into content model and pedagogical model to increase the reusability and flexibility of the system