Managing e-Learning: what are the real implications for schools?

This paper is concerned with the use of e-learning in secondary education. It is based on research that has taken place over a period of two years with students aged 14-16 (Key Stage 4). The paper considers the current research in e-learning and identifies the challenges faced by students, the changing role of the learner, and the impact e-learning can have on students. The author argues that preparation needs to be carried out at the school level prior to introducing e-learning into the Key Stage 4 curriculum. It concludes by discussing the findings of the research which identifies a range of issues schools may want to consider, when embracing e-learning

Progressive assessment of student engagement with web-based guided learning

Purpose – The purpose of this research is to investigate student engagement in guided web-based learning systems.It looks into students' engagement and their behavioral patterns in two types of guided learning systems (i.e. a fully- and a partially-guided).The research also aims to demonstrate how the engagement evolves from the beginning towards the end of the interactions; which enables analysis to be performed on the quality of engagement. Design/methodology/approach – An experimental study was conducted on 41 students from a public university in Malaysia using two web-based systems as the main learning tools.The students' engagement data were captured three times during the interactions and once at the end of the experimental study using student self-report.Findings – The main outcome of this study suggests that student engagement was changing over time either in positive or negative patterns.The directions of change in both types of guided learning were mainly influenced by the students' background of knowledge.Practical implications – This study demonstrates that student engagement is dynamic. Therefore, progressive assessment is a practical approach to obtain the engagement data which can be used to regulate and improve student engagement in web-based systems.As a result, an adaptive and intelligent web-based learning environment can be created. Originality/value – This research proposes a new approach to improve students' engagement in web-based instruction, that is, through a progressive assessment of their current experience

AI-Based Adaptive Learning: A Systematic Mapping of the Literature

With the aid of technology advancement, the field of education has seen a noticeable transformation. The teaching-learning process is now more interactive and is no longer restricted to students' physical presence in the classroom but instead makes use of specialized online platforms. In recent years, solutions that offer learning routes customized to learners' needs have become more necessary. In this regard, artificial intelligence has served as an excellent answer, allowing for the building of educational systems that can accommodate a wide range of student needs. Through this paper, a systematic mapping of the literature on AI-based adaptive learning is presented. The examination of 93 articles published between 2000 and 2022 made it possible to draw several conclusions, including the number of adaptive learning environments based on AI, the types of AI algorithms used, the objectives targeted by these systems as well as factors related to adaptation. This study may serve as a springboard for further investigation into how to address the problems raised by the current state.&nbsp

I’ve (Urn)ed This: An Application and Criterion-based Evaluation of the Urnings Algorithm

There is increased interest in personalized learning and making e-learning environments more adaptable. Some e-learning systems may use an Item Response Theory (IRT)-based assessment system. An important distinction between assessment and learning contexts is that learner proficiency is expected to remain constant across an assessment, while it is expected to change over time in a learning context. Constant learner proficiency during an assessment enables conventional approaches to estimating person and item parameters using IRT. These IRT-based systems could be abandoned for alternative approaches to modeling learners and system learning content, but assessments may provide more functions than adapting learning material to students. Thus, there is the question, how can e-learning systems with IRT-based assessment components more dynamically adapt their learning content? Is there a solution that leverages IRT for adapting the learning content of the system? A promising solution is the Urnings algorithm. Like other candidate algorithms, it is computationally light, but this algorithm has mechanisms for preventing variance inflation and is suitable for e-learning contexts. It also provides a measure of uncertainty around estimates. It has been studied both through simulations and applications to e-learning systems. Results are promising; however, there has not been an application of the Urnings algorithm to an e-learning context where there are conventionally estimated person parameters to compare the algorithm estimates to. This study addresses this gap by applying the Urnings algorithm to a K–8 reading and mathematics learning platform. In data from this platform, we have person parameter estimates across academic years from an in-system diagnostic assessment. Results from this study will help industry researchers understand the feasibility of the Urnings algorithm for large e-learning systems with IRT-based assessment components

Understanding the role of demographic, perceptual and personality factors in the use of mobile data services

Conventional telecommunication technologies, characterised by wires and fixed locations, are rapidly giving way to mobile data services (MDSs). Recent technological developments have opened up possibilities for various applications of MDSs. This thesis specifically focuses on two promising MDS applications: mobile banking (m-banking) and mobile learning (m-learning). It signifies an important step in the testing of theories related to demographics, perceptions and personality traits in the use of MDSs, through three studies. In Study 1, which examines the digital divide in the use of MDSs, I analyse the effects of gender and age differences on the usage of MDSs. An online survey was disseminated in the United Kingdom (UK), and completed responses were received from 2,000 mobile phone users and non-users on both sides of the divide (i.e., with or without access to mobile information and communication technologies (IeTs)). I developed eight hypotheses and used logistic regression and chi-square tests to test them. My findings demonstrate that men are more likely than women to use MDSs, and that young people are more likely than their older counterparts to use MDSs. The study contributes to the literature on MDSs by highlighting the issue of the digital divide. The study also provides insights to MDS providers and policymakers on how to develop and promote MDSs for different socio-demographic groups. In Study 2, I examine m-banking which is regarded as a killer application amongst all MDSs. This study has two parts. In Study 2 (Part I), I present a literature review of, and a classification framework for, the existing m-banking literature. Sixty-five articles related to m-banking were published in major journals and presented at conferences between January 2000 and June 2010. They belong to various disciplines, including information systems (IS), technology innovation, management and marketing. Study 2 (Part I) classifies these articles into five main categories: (1) m-banking overview and conceptualisation; (2):) m-banking applications and cases; (3) m-banking behaviour; (4) m-banking infrastructures; and (5) m-banking strategic, legal and ethical issues. Several new research questions that could yield valuable results in the m-banking field are given, including a fundamental question on users' switching behaviour from other banking channels to m-banking which is examined in further detail in Study 2 (Part II). In Study 2 (Part II), I develop a model that is anchored by expectancy theory and validate it using data collected from 493 mobile phone users in order to predict intentions to switch to m-banking. I chose the m-banking context because recent advances in mobile devices have made m-banking an attractive option for banks and mobile service providers; however, consumer demand for m-banking is low. The findings suggest that perceived mobility, relative advantage and self-efficacy are positively related to user intentions to switch banking channels. Perceived complexity is negatively related, whereas perceived financial resources and risk are not related to intentions to switch. Study 3 examines another key MDS, m-leaming which proposes to use a text messaging service as a tool to stimulate learners' activities. It examines whether learners' personalities influence their reactions to accessing course materials through m-learning messages. The Myers-Briggs Type Indicator (MBTI) was used to categorise learners into different personality groups. After conducting a field study with 217 students, it was found that learners of different personalities showed different levels of activities when receiving m-learning messages

Computational Psychometrics for Item-based Computerized Adaptive Learning

With advances in computer technology and expanded access to educational data, psychometrics faces new opportunities and challenges for enhancing pattern discovery and decision-making in testing and learning. In this dissertation, I introduced three computational psychometrics studies for solving the technical problems in item-based computerized adaptive learning (CAL) systems related to dynamic measurement, diagnosis, and recommendation based on Bayesian item response theory (IRT). For the first study, I introduced a new knowledge tracing (KT) model, dynamic IRT (DIRT), which can iteratively update the posterior distribution of latent ability based on moment match approximation and capture the uncertainty of ability change during the learning process. For dynamic measurement, DIRT has advantages in interpretation, flexibility, computation cost, and implementability. For the second study, A new measurement model, named multilevel and multidimensional item response theory with Q matrix (MMIRT-Q), was proposed to provide fine-grained diagnostic feedback. I introduced sequential Monte Carlo (SMC) for online estimation of latent abilities. For the third study, I proposed the maximum expected ratio of posterior variance reduction criterion (MERPV) for testing purposes and the maximum expected improvement in posterior mean (MEIPM) criterion for learning purposes under the unified framework of IRT. With these computational psychometrics solutions, we can improve the students’ learning and testing experience with accurate psychometrics measurement, timely diagnosis feedback, and efficient item selection

Reference model for adaptive and intelligent educational systems supported by learning objects

Abstract: Computer Aided Learning, known more widely with the generic name of e-learning, has become a powerful tool with lots of potentialities within educational field. Even though, one of the main critics that it receives is that in most cases the implemented courses follows a “one size fits all” approach, which means that all students receive the same content in the same way being unaware of their particular needs. This problem is not due only to the absence of direct interaction between student and tutor, but also because of the lack of an appropriate instructional design. There are several approaches which deal with this issue and look for adapt the teaching process to students. One could say that in the top of those approaches the Adaptive and Intelligent Educational Systems are situated, which merges the functionalities of two approaches: the Adaptive Educational Hypermedia Systems and the Intelligent Tutoring Systems. Nevertheless, after an extensive literature review, a major inconvenience is still found for this kind of systems and particularly for their reference models: or they are too simple, including just a few functionalities; or they are too complex, which difficult their design and implementation. Considering this panorama, the main objective of this dissertation thesis was the definition of a reference model trying to reach such an elusive equilibrium, in such a way that allows the design of courses which adapt themselves in an intelligent and effective way to the progress and characteristics of each student but without being too complex. Another important feature is that this model integrates Learning Objects, promoting this way flexibility and reusability. In order to achieve this general objective, three sub-models were considered: a domain model, a student model and a tutor model. The first one serves to structure the knowledge domain and was defined using the notion of learning goal and a flexible multilevel schema with optional prerequisite operations. The second one aids to characterize students and considered personal, knowledge and psycho-cognitive information. The third one may be considered as the hearth of the system and defines the adopted adaptive functionalities: sequencing and navigation, content presentation, assessment, and collaborative support. With the aim of clarify the three sub-models, as well as all their components and relationships, an instantiation example was also presented. Such an instantiation was called Doctus, an authoring tool for adaptive courses. Doctus was not only helpful to exemplify the setup of the referece model as a whole, but also to refine sub-models and several procedures envolved. As final part of the dissertation, the implementation and preliminary validation of Doctus was performed. This was done with 51 subjects, teachers from different formation levels. The obtained results in this stage were outstanding, all the adaptive functionalities were well evaluated and all of those polled felt enthusiastic about counting with a tool for helping them in their teaching practices considering students as particular individuals.Doctorad