9,910 research outputs found

    A Preliminary Study of Integrating Flipped Classroom strategy for Classical Chinese Learning

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    [[abstract]]This is a multiphase study which aims to investigate how to provide learners with an method to acquire classical Chinese through integrating mobile technology with the flipped classroom approach. Currently, in the first phase of study, the researcher adopts informant design through questionnaire survey to understand students' and instructors' perceptions of using mobile learning devices for classical Chinese learning, and afterwards the researcher constructs the system based on the pilot results. The pilot questionnaire results, structure of the developed mobile learning system and the practical application of the developed system for classical Chinese teaching and learning are described in the paper.[[notice]]補正完

    Learn Smarter, Not Harder – Exploring the Development of Learning Analytics Use Cases to Create Tailor-Made Online Learning Experiences

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    Our world is significantly shaped by digitalization, fostering new opportunities for technology-mediated learning. Therefore, massive amounts of knowledge become available online. However, concurrently these formats entail less interaction and guidance from lecturers. Thus, learners need to be supported by intelligent learning tools that provide suitable knowledge in a tailored way. In this context, the use of learning analytics in its multifaceted forms is essential. Existing literature shows a proliferation of learning analytics use cases without a systematic structure. Based on a structured literature review of 42 papers we organized existing literature contributions systematically and derived four use cases: learning dashboards, individualized content, tutoring systems, and adaptable learning process based on personality. Our use cases will serve as a basis for a targeted scientific discourse and are valuable orientation for the development of future learning analytics use cases to give rise to the new form of Learning Experience Platforms

    A Systematic Literature Review of Empirical Studies on Learning Analytics in Educational Games

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    Learning analytics (LA) in educational games is considered an emerging practice due to its potential of enhancing the learning process. Growing research on formative assessment has shed light on the ways in which students' meaningful and in-situ learning experiences can be supported through educational games. To understand learners' playful experiences during gameplay, researchers have applied LA, which focuses on understanding students' in-game behaviour trajectories and personal learning needs during play. However, there is a lack of studies exploring how further research on LA in educational games can be conducted. Only a few analyses have discussed how LA has been designed, integrated, and implemented in educational games. Accordingly, this systematic literature review examined how LA in educational games has evolved. The study findings suggest that: (1) there is an increasing need to consider factors such as student modelling, iterative game design and personalisation when designing and implementing LA through educational games; and (2) the use of LA creates several challenges from technical, data management and ethical perspectives. In addition to outlining these findings, this article offers important notes for practitioners, and discusses the implications of the study’s results

    Cognitive and affective perspectives on immersive technology in education

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    This research explains the rationale behind the utilization of mobile learning technologies. It involves a qualitative study among children to better understand their opinions and perceptions toward the use of educational applications (apps) that are available on their mobile devices, including smartphones and tablets. The researchers organized semi-structured, face-to-face interview sessions with primary school students who were using mobile technologies at their primary school. The students reported that their engagement with the educational apps has improved their competencies. They acquired relational and communicative skills as they collaborated in teams. On the other hand, there were a few students who were not perceiving the usefulness and the ease of use of the educational apps on their mobile device. This study indicates that the research participants had different skillsets as they exhibited different learning abilities. In conclusion, this contribution opens-up avenues for future research in this promising field of study.peer-reviewe

    FinCraft: Immersive Personalised Persuasive Serious Games for Financial Literacy Among Young Decision-Makers

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    FinCraft is an open source gaming platform to enhance financial literacy. Research has shown a growing concern for financial literacy and financial decision-making across several age groups and interventionist programme methods. However, very few focused on the youth demographic specifically. Fewer, considered fun and sustainable means of financial literacy enhancement. In a day and age of instant gratification, people gravitate towards familiar and engaging content. In this paper, we posit starting financial literacy enhancement early - during the teenage years of individuals, so secure saving and spending habits can be adopted prior to adult years and responsibility. Through gamification, FinCraft aims to bridge the familiarity and engagement gap that makes financial literacy, an unexplored, significant part of growing up. We propose various conceptual and system artefacts at the intersection of serious games and learning analytics for financial literacy

    Re-Defining, Analyzing and Predicting Persistence Using Student Events in Online Learning

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    This article belongs to the Special Issue Smart LearningIn education, several studies have tried to track student persistence (i.e., students' ability to keep on working on the assigned tasks) using di fferent definitions and self-reported data. However, self-reported metrics may be limited, and currently, online courses allow collecting many low-level events to analyze student behaviors based on logs and using learning analytics. These analyses can be used to provide personalized and adaptative feedback in Smart Learning Environments. In this line, this work proposes the analysis and measurement of two types of persistence based on students' interactions in online courses: (1) local persistence (based on the attempts used to solve an exercise when the student answers it incorrectly), and (2) global persistence (based on overall course activity/completion). Results show that there are different students' profiles based on local persistence, although medium local persistence stands out. Moreover, local persistence is highly a ffected by course context and it can vary throughout the course. Furthermore, local persistence does not necessarily relate to global persistence or engagement with videos, although it is related to students' average grade. Finally, predictive analysis shows that local persistence is not a strong predictor of global persistence and performance, although it can add some value to the predictive models.This work was partially funded by FEDER/Ministerio de Ciencia, Innovación y Universidades - Agencia Estatal de Investigación/project Smartlet (TIN2017-85179-C3-1-R), and by the Madrid Regional Government, through the project e-Madrid-CM (S2018/TCS-4307). The latter is also co-financed by the Structural Funds (FSE and FEDER). This work received also partial support by Ministerio de Ciencia, Innovación y Universidades, under an FPU fellowship (FPU016/00526)
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