Our daily lives have been transformed by mobile smart devices. Due to the sudden impact of the coronavirus (Covid-19) on education, the importance of mobile devices for communicating with teachers and students has risen to a new level of prominence. The Web of Science and Scopus databases were used to conduct a systematic review of the research on mobile collaborative learning in engineering education. The purpose of this review is to ascertain the degree to which research on mobile collaborative learning has been conducted in the field of engineering education between 2010 and 2020. A total of 48 articles were reviewed to ascertain the research methodologies and area of study, as well as to provide an updated review of studies on mobile collaborative applications, particularly in the field of engineering education. Among the most significant findings is that the majority of publications make use of augmented reality and mobile application development. According to the review, the majority of studies were conducted in the fields of computer sciences, electronic engineering, and artificial intelligence.
Knowledge is the fundamental resource that allows us to function intelligently. Similarly, organizations typically use different types of knowledge to enhance their performance. Commonsense knowledge that is not well formalized modeling is the key to disaster management in the process of information gathering into a formalized way. Modeling commonsense knowledge is crucial for classifying and presenting of unstructured knowledge. This paper suggests an approach to achieving this objective, by proposing a three-phase knowledge modeling approach. At the initial stage commonsense knowledge is converted into a questionnaire. Removing dependencies among the questions are modeled using principal component analysis. Classification of the knowledge is processed through fuzzy logic module, which is constructed on the basis of principal components. Further explanations for classified knowledge are derived by expert system technology. We have implemented the system using FLEX expert system shell, SPSS, XML, and VB. This paper describes one such approach using classification of human constituents in Ayurvedic medicine. Evaluation of the system has shown 77% accuracy.
PurposeThe purpose of this study is to identify the learner characteristics attributable to the likelihood and the duration of programme completion in the Bachelor of Science (BSc) and Bachelor of Technology Honours in Engineering (BTech) degree programmes of the Open University of Sri Lanka (OUSL).Design/methodology/approachData were gathered from the re-registrants for the degree programmes in the academic year 2020/2021, using a questionnaire developed as a Google form. The sample consisted of 301 and 516 re-registrants from the BTech and BSc programmes respectively. Influential factors were identified using Kruskal Wallis test (for duration of completion), binary logistic regression (for likelihood of completion) and Chi-squared test (associations between presage and process factors).FindingsEntry qualification, age and time management skills at entry had significant effects on duration of completion. Attendance at academic activities, organizing time for self-studies and the competency in English at enrolment had significant effects on the likelihood of completion. Prior open and distance learning (ODL) experience had no significant effect on any of the product factors considered.Research limitations/implicationsInaccessibility of dropouts and using only the responses from the first administration of the questionnaire are limitations. Active learners are more likely to respond, in the first administration and may bias the results.Practical implicationsFindings are useful for designing future studies to identify at-risk students and thereby enhance the programme completion and reduce prolonged time for completion.Social implicationsEffective strategies to control the identified factors will uplift programme completion and reduce drop-out rates.Originality/valueDecision making using inferential techniques makes the study distinct among studies undertaken on the same population. The study enriches the limited current research on factors affecting programme completion in ODL mode.
Knowledge modelling gives the intention of knowledge engineering which applicable for managing information systems. Tacit knowledge is the key issue of knowledge modelling aspect because all knowledge is rooted in tacit knowledge. This paper presents a research, which is incorporated of modelling of tacit knowledge. Here we have used an Intelligent Hybrid system for developing an approach for modelling tacit knowledge. The Intelligent Hybrid system is involved with artificial intelligent techniques, namely fuzzy logic and expert system technology. We primarily used fuzzy logic together with statistical technique of principle component analysis for modelling tacit domains. Tacit knowledge in Ayurvedic sub-domain of individual classification has been acquired through a questionnaire and analysed to identify the dependencies, which lead to make tacit knowledge in the particular domain. In the first place analysis was done using statistical techniques of principle components and the results were not compatible with the experiences of Ayurvedic experts. As such, fuzzy logic has been used to further model the Ayurvedic sub-domain. The result of the modelling of Ayurvedic domain using fuzzy logic has been compatible with the experiences of the Ayurvedic experts. It has shown 77% accuracy in using the tacit knowledge for reasoning in the relevant domain. The development has been done using Visual basic, FLEX expert system shell and the system runs on Windows platform. The Intelligent Hybrid system has been successfully applied for several tacit domains. Performances were very close to handling tacit knowledge by the human expert in tacit domain.
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