Mobile game-based learning constitutes a hot issue in the related scientific literature since it promotes learning through an entertaining way and fosters student motivation to increase engagement in ...the educational process. As such, it can enhance the learning process and improve student participation. Towards this direction, this paper investigates how mobile learning and game-based learning can be utilized in higher education settings and analyzes the pedagogical affordance of their adoption. As a testbed for our research, we designed and implemented Quiz Time! which is an intelligent mobile game-based learning application for assessing and advancing learners' knowledge in the programming language C#. Quiz Time! employs an assessing knowledge module for testing the knowledge of learners, a vectorial-based recommendation module for proposing personalized collaboration in group playing, a dynamic fuzzy logic-based advice generator for tailored assistance to learners' profile and misconceptions, and a cognitive learner modeler supporting the aforementioned modules. Quiz Time! was used in a higher education institution for an academic semester and was evaluated by students and computer science experts using an established framework and the statistical hypothesis test. Regarding the evaluation results, the computer science experts validated the pedagogical adequacy of the application and the students highlighted its positive impact on learning and its usefulness. A major conclusion is that incorporating personalization and collaboration in mobile game-based learning can further assist students in higher education towards advancing their knowledge level.
•Edutainment advances students' knowledge level in higher education.•Mobile game-based learning promotes students' engagement in higher education.•Personalized recommendation for collaboration increases students' learning outcome.•Tailored advice upgrades students' knowledge and skills and decreases dropout rates.•Fuzzy logic promotes individualized learning environments supporting higher education.
In this study, the trends and developments of technology-enhanced adaptive/personalized learning have been studied by reviewing the related journal articles in the recent decade (i.e., from 2007 to ...2017). To be specific, we investigated many research issues such as the parameters of adaptive/personalized learning, learning supports, learning outcomes, subjects, participants, hardware, and so on. Furthermore, this study reveals that personalized/adaptive learning has always been an attractive topic in this field, and personalized data sources, for example, students’ preferences, learning achievements, profiles, and learning logs have become the main parameters for supporting personalized/adaptive learning. In addition, we found that the majority of the studies on personalized/adaptive learning still only supported traditional computers or devices, while only a few studies have been conducted on wearable devices, smartphones and tablet computers. In other words, personalized/adaptive learning has a significant number of potential applications on the above smart devices with the rapid development of artificial intelligence, virtual reality, cloud computing and wearable computing. Through the in-depth analysis of the trends and developments in the various dimensions of personalized/adaptive learning, the future research directions, issues and challenges are discussed in our paper.
•A systematic review of adaptive/personalized learning from 2007 to 2017 is conducted.•A comprehensive coding scheme is developed based on the constructivism.•Research issues like learning supports, learning outcomes and so on are addressed.•The results in all categories of the coding scheme are discussed.•Future trends and applications of adaptive/personalized learning are analyzed.
Discovering useful hidden patterns from learner data for online learning systems is valuable in education technology. Studies on personalized learning full-path recommendation are particularly ...important for the development of advanced E-learning systems. In this paper, we present a novel model of full-path learning recommendation. This model relies on clustering and machine learning techniques. Based on a feature similarity metric on learners, we first cluster a collection of learners and train a long short-term memory (LSTM) model in order to predict their learning paths and performance. Personalized learning full-paths are then selected from the results of path prediction. Finally, a suitable learning full-path is recommended specifically to a test learner. In this study, a series of experiments have been carried out against learning resource datasets. By comparisons, experimental results indicate that our proposed methods are able to make sound recommendations on appropriate learning paths with significantly improved learning results in terms of accuracy and efficiency.
Personalized learning (PL) has been promoted as a major aim and reform effort across the contemporary education system. In this article, we systematically identified and synthesized 71 empirical ...studies associated with the implementation of PL that were conducted between 2006 and 2019. This synthesis examined the current research efforts on the PL implementation with a focus on the primary purposes, overall methodological characteristics, and associated student learning outcomes of the identified studies. Using the method of critical interpretive synthesis, we identified two overarching themes in relation to PL across various disciplines of study. The two themes included investigating (a) the role of varying technologies and (b) contextual factors that impacted the implementation of PL. However, few studies have examined the effects of PL as a whole-school initiative on student educational outcomes. We ended by discussing the issues with the conceptualization and empirical evidence of PL and providing implications for the future advancement of the field.
•The paper reviewed 71 studies on the implementation of personalized learning (PL).•Many studies explored the role of technology in supporting PL implementation.•Few studies examined the effectiveness of PL as a whole-school initiative.•Issues around PL conceptualization and empirical evidence were discussed.
Whole-of-course approaches have emerged as an important topic in the contemporary outcomes-based education environment. While research-informed accounts of whole-of-course approaches exist, most take ...undergraduate education as the default. Few examples feature postgraduate education where students seek career enhancement rather than entry. Employing case study methodology, this paper discusses an innovative whole-of-course approach taken in the design and delivery of an Australian postgraduate education course. What marks it as innovative, is that the whole-of-course approach consists of two interrelated strands: One follows a (more familiar) whole-of-course practice of scaffolding graduate attributes, course and subject learning outcomes and is primarily driven by university interests and academics. A second whole-of-course process works alongside the first but is driven by postgraduates' professional practice and career goals. The paper concludes by suggesting that a whole-of-course approach to design combined with a whole-of-course student process can reconcile postgraduates' learning needs with the interests of the university.
A core focus of self‐regulated learning (SRL) research lies in uncovering methods to empower learners within digital learning environments. As digital technologies continue to evolve during the ...current hype of artificial intelligence (AI) in education, the theoretical, empirical and methodological nuances to support SRL are emerging and offering new ways for adaptive support and guidance for learners. Such affordances offer a unique opportunity for personalised learning experiences, including adaptive interventions. Exploring the application of adaptivity to enhance SRL is an important and emerging area of research that requires further attention. This editorial introduces the contributions of seven papers for the special section on adaptive support for SRL within digital learning environments. These papers explore various themes related to enhancing SRL strategies through technological interventions, offering valuable insights and paving the way for future advancements in this dynamic area.
Intelligent education systems have enabled personalized learning (PL). In PL, students are presented with educational contents that are consistent with their personal knowledge states (KS), and the ...critical task is accurately estimating these states through data. Knowledge tracing (KT) infers KS (latent) through historical student interactions (observed) with the knowledge components (KCs). A wide variety of KT techniques have been developed, from Bayesian Knowledge Tracing (BKT) to Deep Knowledge Tracing (DKT). However, in most of these methods, the KCs are represented as stand-alone entities, and the effect of representing KCs using contexts such as learning-related factors has been under-investigated. Also, KT needs to generate personalized results to facilitate tasks such as exercise recommendation. In this paper, we propose two approaches that use a contextualized representation of KCs, one with a content-based approach and another with a Long Short Term Memory (LSTM) network plus a personalization mechanism. By performing extensive experiments on two real-world datasets, results show not only a tangible improvement in prediction accuracy in the KT task compared to existing methods, but also its effectiveness in improving the recommendation precision.
Introduction: Digital health educational features have the potential to improve engagement and glycemic outcomes in individuals with diabetes by providing improved knowledge, increased motivation, ...and personalized learning. Engagement actions are well evaluated by digital monitoring or by logging iterations, and clinical outcomes by target range measurements. A new learning feature in the Dario application included educational videos and learning materials including healthy eating habits, activity, and stress relief. This study evaluated the effect of implementing such a feature on users’ engagement and clinical outcomes.
Method: A retrospective data evaluation study was performed on Dario TM members who experienced the educational feature. Engagement (blood glucose measurements and logging food to Dario app) and glycemic outcomes were assessed three months pre-post experiencing the feature.
Results: A group of 994 people with type 2 and prediabetes who were active in the app and measured their blood glucose 3 months before using the new feature and in the following 3 months after, was evaluated. The average number of measurements was increased by 34% (P<0.05) following the introduction of the new learning feature. A subgroup of 303 users that reported depression as a co-existing condition increased food logging events by 39%. In a subgroup of 234 high-risk users (baseline >180 mg/dL) the average blood glucose and glucose variability were significantly reduced by 13% and 11% on average, respectively (p<0.05).
Conclusion: The present study demonstrates that by providing improved knowledge, increased motivation, and personalized learning digital health educational features can help users feel more empowered to manage their diabetes effectively.
Disclosure
Y. Hershcovitz: Employee; DarioHealth Corp. A. Gurewitz: None. O. Manejwala: Employee; DarioHealth Corp. Stock/Shareholder; DarioHealth Corp.
