International Journal of Learning Technology (11 papers in press)
Role of extrinsic and intrinsic motivation on satisfaction in e-learning in Kuwait: an empirical research
by Rabab Dawoud Alsaffar
Mapping Learning Management System Features to Persuasive Design Strategies to Inform the Design of Persuasive Learning Management System
by Wan Nooraishya Wan Ahmad, Ahmad Rizal Ahmad Rodzuan, Carolyn Salimun
Abstract: The term persuasive indicates the effect of a relevant subject or object on others through persuasion technique. It derives from the domain of persuasive technology that focuses on using technology to shape or change human behaviour/attitude. In recent years, persuasion has become an essential topic in designing human-computer interaction. This paper seeks to shed light on the emergence and consolidation of persuasion in the design of online learning environments and their effect on the users. We synthesise the literature on online learning by reviewing the literature on persuasive design in the online learning context. Based on the review, we identified several persuasive strategies that are used effectively in online learning. These persuasive design strategies include reduction, tunnelling, tailoring, self-monitoring, liking, personalisation, social role, reward, praise, suggestions, trustworthiness, competition, and social comparison. A mapping of possible persuasive design for a learning management system is suggested.
Keywords: persuasive; learning management system; e-learning; online learning.
Using the Personal Computer utilization model to predict students technology user behaviour in universities in Botswana
by Norman Rudhumbu, Kanos Matyokurehwa, Cross Gombiro
Abstract: Technological advances have reduced learning and teaching limits based on time and distance making education more accessible to students. Accordingly, this study therefore sought to establish whether the Personal Computer utilization model (PCUM) could be used to predict and explain factors that influence the technology user behaviour of university students in Botswana. A quantitative approach that employed a self-constructed structured questionnaire was used to collect data from a sample of 940 students from three public universities. Confirmatory Factor Analysis (CFA) was used for data validation of scale items. Results of the study showed that five out of the six dimensions of the PCUM significantly influenced the technology user behaviour of university students that showing that overall, the PCUM could be used to predict and explain the technology user behaviour of university students. These results have implications on both practice and policy with regards to the integration of technology in universities.
Keywords: Job-fit; affect towards technology use; Personal Computer utilization model; technology user behaviour; social factors; facilitating conditions.
The role of technology readiness in students' perception and behaviour towards e-learning technologies
by Tejas R. Shah, Tejal T. Shah
Abstract: The objective of this research is to explore and analyze the role of technology readiness (TR) in students' perception and behaviour of e-learning technologies. This research also aims to measure e-learning service quality. A structural equation modelling (SEM) technique was used for data analysis. First, the literature review related to TR, e-learning service quality, e-learning behaviour was conducted. Second, Delphi method was used to refine questionnaire. Survey method was followed for data collection. The final sample included of students who had frequent experience of using e-learning technologies. Results indicate the role of role of TR and e-learning service quality in students e-learning behaviour. TR should be given increased attention by universities and educational institutions to improve students perception and behaviour of e-learning technologies. Universities and educational institutions need to assess e-learning service quality to improve students satisfaction and behaviour intention towards e-learning technologies.
Keywords: technology readiness; service quality; e-learning; satisfaction; behaviour intention; education; India.
Mapping of Learning Style with Learning Object Metadata for Addressing Cold-start Problem in E-learning Recommender Systems
by Jeevamol Joy, Renumol V G
Abstract: In the e-learning domain, content recommender systems were evolved to recommend relevant learning content based on the learner preferences. One of the significant drawbacks of content recommenders in the e-learning domain is the new user cold-start problem. The objective of this study is to propose a recommendation model for addressing the cold-start problem using learner's learning style alone. Learning style refers to the way a learner prefers to learn and it is a prominent learner characteristic to understand the learner profile. In this study, we propose an ontology-based recommendation algorithm that makes use of the learning dimensions of the Felder Silverman Learning Style Model to map with the learning object characteristics. The knowledge about the learner and the learning objects are represented using ontologies. Experiments were conducted to evaluate the accuracy of the proposed recommendation model using the evaluation metric, f-measure. The learner satisfaction with the proposed model is measured based on the ratings given to the learning objects by the participants of the experiment.
Keywords: Recommender System; Cold-start; Learning Style; Learning Object Metadata; Learning Management System; Ontology.
Exploring adult learners perceptions towards learning English via mobile applications
by Jo Shan Fu, Hui-Chin Yeh, Pei-Chun Liang, Leechin Heng
Emotion AI in Education: A Literature Review
by Stefan Reindl
Abstract: Emotion (or affective) AI is a hot topic within the greater field of AI, in both, academic as well as practitioner circles. One of the industries with great potential for AI implementation is education. While emotion AI is commonly referred to as a field of growing interest, research in the specific context of education is still in its early stages and publications are few. This paper aims to discuss this emerging field of research on emotion artificial intelligence in the context of education. The current body of literature can be grouped into three clusters: (1) Concept and model development, (2) Intelligent tutoring systems, and (3) Students state of mind. The review concludes that emotion-based improvements of learning systems surely hold a lot of promise yet still suffer one major shortcoming: that of appropriate responses to the detected emotions.
Keywords: Emotion AI; Affective AI; Artificial Intelligence; Education.
What determines students behavioural intention to use mobile learning management system? Empirical answers from a blended environment in sub-Saharan Africa.
by Emmanuel Arthur-Nyarko, Stephen Brobbey Gyan, Alexander Asante
Abstract: Using mobile devices for the delivery of instruction is progressively gaining the attention of many researchers in recent times. This study investigated the determinants of students' behavioural intention (BI) to use Mobile Learning Management Systems (M-LMS) in a blended learning environment. The study was undertaken at the College of Distance Education (CoDE) of the University of Cape Coast (UCC) in Ghana. Using a predictive correlational design, a 28-item questionnaire based on the Extended Technology Acceptance Model (E-TAM) was used to gather data from 370 students, for which 98% return rate was achieved. The data were analysed using descriptive statistics and multiple linear regression with the stepwise method. The findings of the study revealed that distance learning students at the College held a positive behavioural intention to use M-LMS for learning, to support face-to-face engagement. It was also revealed that factors such as perceived ease of use (PEOU), perceived usefulness (PU), perceived educational compatibility (PEC), and facilitating conditions (FC) were significant determinants of students' behavioural intention to use M-LMS for learning. Moreover, PEOU was the best predictor of students behavioural intention to use M-LMS in a blended distance environment, explaining about 44% of the variations in the dependent variable with FC being the least predictor. The study provides useful recommendations for sustainable M-LMS implementation at the College and in similar environments.
Keywords: behavioural intention; mobile learning; learning management system; blended learning; TAM.
Media profiles and transmedia learning in university students
by Meritxell Estebanell-Minguell, Juan González-Martínez, Moisès Esteban-Guitart, Elisabet Serrat-Sellabona
Abstract: Taking the consideration that university students learning occurs in both formal and informal situations as a starting point, the present study focuses on investigating the media profile of these students and the relationship this profile has with learning. The study participants comprised 733 university students who answered an online questionnaire related to their media literacy skills, transmedia practices and learning practices through transmedia resources. The main results show that those students who learn most through the media are more critical; that is, they are actively involved in the creation of transmedia content but are critical in both their consumption and production of such content. In addition, other traits can be added to the profile of students who learn in informal transmedia contexts. The results are discussed in relation to the approaches employed in this new learning ecology.
Keywords: media competences; transmedia learning; university students.
Personalized Instructional Feedback in a Mobile-Assisted Language Learning Application Using Fuzzy Reasoning
by Konstantina Chrysafiadi, Christos Troussas, Marina Virvou
Abstract: This paper addresses the interesting issue of mobile-assisted language learning using novel techniques for further improving the adaptivity and personalization to students. The domain model of the system includes English and French language concepts, and its user model holds information about students and their progress. It also embodies a database of categories of errors and misconceptions which have been reported as common in the related literature. The system is also responsible for conducting model-based error diagnosis using machine learning techniques and identifying errors such as knowledge transfer, spelling or verb mistakes, etc. In conjunction with error diagnosis, the system employs fuzzy logic to automatically model these misconceptions and errors and then provide personalized feedback to students based on their personal learning needs. The system has been fully evaluated, using the CIAO! framework and t-test. The evaluation results are positive and encouraging regarding the educational effectiveness.
Keywords: Mobile-assisted language learning (MALL); error diagnosis; adaptive learning; fuzzy logic; adaptive feedback; multiple foreign language learning; second language acquisition.
The challenges of distance assessment in higher education a case study
by Viktorija Florjancic
Abstract: The article presents the challenges of introducing e-assessment at a traditional university where teachers did not extensively use online learning until the first wave of the COVID-19 epidemic. The ad-hoc switch to online learning environments is known as emergency remote teaching because it was not planned. The first research supports other authors findings that even teachers who already skilled ICT users in pedagogical practice faced some problems. Most of the challenges are related to exam proctoring. After a year of emergency remote teaching, the second survey results show a positive impact on education teachers became more confident, developed new teaching methods, and became more skilled ICT users. Teachers exchanging of best practices seems to be an essential lever for the teachers improvement. The cheating issues are still a focus.
Keywords: distance exams; COVID-19; higher education; Slovenia.