Empirical validation of the OCRAT framework. A holistic and systematic guideline for audio-only instructional design.

This work empirically validates 23 audio-only instructional guidelines regrouped under the Organisation Content Retention Auditory and Technology (OCRAT) framework. A total of 10 audio courses consisting of 40 audio lessons/chapters equivalent to around 3 h on various topics in agriculture and fisheries were devised based on the OCRAT guidelines. A group of 225 and 180 literate and semi-literate populations both in Mauritius and India were identified. Through a range of ten (10) courses regrouping the respective audio lessons, the framework was tested over an ACALEs platform. A survey was conducted on learners who participated and completed the course. From the collected data, empirical testing using inferences on population proportions based on the binomial distribution was conducted to validate the proposed set of guidelines. The results proved to be very conclusive. Moreover, this work elaborates even further on the validated guidelines and substantiates them with additional literature. Henceforth, it can confidently be used by instructional designers for the development of interactive audio-only courses not only on ACALEs platforms but even with conventional audio-based learning like radio programs.

Personalized and Adaptive Context-Aware Mobile Learning: Review, challenges and future directions.

Due to the outbreak of COVID 19, digital learning has become the most efficient learning and teaching technique adopted across the world. The pervasiveness of Personalized and Adaptive Context-Aware Mobile Learning (PACAML) technologies is improving the academic performances of learners by providing an efficient learning platform that supports social interactivity, context sensitivity, connectivity, and individuality in a ubiquitous manner. Several studies have demonstrated the efficacy of PACAML in a modern and innovative educational environment. Based on the recent studies and development of mobile learning technologies, there is clearly a gap in the research that provides a comprehensive body of knowledge on PACAML. In this paper, a review has been conducted on the existing PACAML, analyzing the recent research and development progress using Kitchenham et al. (2009) for systematic reviews. The review was conducted on 25 papers which were selected using the PRISMA technique to put forward the quality criteria that are based on the research aims, objectives and knowledge relevant to the study of PACAML. The results identified the contextual information used in the PACAML studies, the infrastructural requirements of PACAML, the application of PACAML in functional educational settings and the major methodological approaches applied in the studies of PACAML. Finally, the paper presents challenges and future directions that will be of interest to researchers in the educational technologies in the context of PACAML.

Literature-based discovery approaches for evidence-based healthcare: a systematic review.

PURPOSE: Literature-Based Discovery (LBD) is a text mining technique used to generate novel hypotheses from vast amounts of literature sources, by identifying links between concepts from disparate sources. One of the main areas where it has been predominantly applied is the healthcare domain, whereby promising results, in the form of novel hypotheses, have been reported. The purpose of this work was to conduct a systematic literature review of recent publications on LBD in the healthcare domain in order to assess the trends in the approaches used and to identify issues and challenges for such systems. METHODS: The review was conducted following the principles of the Kitchenham method. The selected studies have been scrutinized and the derived findings have been reported following the PRISMA guidelines. RESULTS: The review results reveal useful information regarding the application areas, the data sources considered, the approaches used, the performance in terms of accuracy and reliability and future research challenges. The results of this review will be beneficial to LBD researchers and other stakeholders in the healthcare domain, by providing them with useful insights on the approaches to adopt, data sources to consider, evaluation model to use and challenges to reflect on. CONCLUSION: The synthesis of the results of this work has shed light on recent issues and challenges that drive new LBD models and provides avenues for their application in other diverse areas in the healthcare domain. To the best of our knowledge, no such recent review has been conducted.

Recent advances in computational tools and resources for the self-management of type 2 diabetes.

Background: While healthcare systems are investing resources on type 2 diabetes patients, self-management is becoming the new trend for these patients. Due to the pervasiveness of computing devices, a number of computerized systems are emerging to support the self-management of patients.Objective: The primary objective of this review is to identify and categorize the computational tools that exist for the self-management of type 2 diabetes, and to identify challenges that need to be addressed.Results: The tools have been categorized into web applications, mobile applications, games and ubiquitous diabetes management systems. We provide a detailed description of the salient features of each category along with a comparison of the various tools, listing their challenges and practical implications. A list of platforms that can be used to develop new tools for the self-management of type 2 diabetes, namely mobile applications development, sensor development, cloud computing, social media, and machine learning and predictive analysis platforms, are also provided.Discussions: This paper identifies a number of challenges in the existing categories of computational tools and consequently presents possible avenues for future research. Failure to address these issues will negatively impact on the adoption rate of the self-management tools and applications.