Social network analysis (SNA) might have an unexplored value in the study of interactions in technology-enhanced learning at large and in online (Problem Based Learning) PBL in particular. Using SNA ...to study students' positions in information exchange networks, communicational activities, and interactions, we can broaden our understanding of the process of PBL, evaluate the significance of each participant role and learn how interactions can affect academic performance. The aim of this study was to study how SNA visual and mathematical analysis can be sued to investigate online PBL, furthermore, to see if students' position and interaction parameters are associated with better performance.
This study involved 135 students and 15 teachers in 15 PBL groups in the course of "growth and development" at Qassim University. The course uses blended PBL as the teaching method. All interaction data were extracted from the learning management system, analyzed with SNA visual and mathematical techniques on the individual student and group level, centrality measures were calculated, and participants' roles were mapped. Correlation among variables was performed using the non-parametric Spearman rank correlation test.
The course had 2620 online interactions, mostly from students to students (89%), students to teacher interactions were 4.9%, and teacher to student interactions were 6.15%. Results have shown that SNA visual analysis can precisely map each PBL group and the level of activity within the group as well as outline the interactions among group participants, identify the isolated and the active students (leaders and facilitators) and evaluate the role of the tutor. Statistical analysis has shown that students' level of activity (outdegree r
(133) = 0.27, p = 0.01), interaction with tutors (r
(133) = 0.22, p = 0.02) are positively correlated with academic performance.
Social network analysis is a practical method that can reliably monitor the interactions in an online PBL environment. Using SNA could reveal important information about the course, the group, and individual students. The insights generated by SNA may be useful in the context of learning analytics to help monitor students' activity.
Deep network architectures struggle to continually learn new tasks without forgetting the previous tasks. A recent trend indicates that dynamic architectures based on an ex-pansion of the parameters ...can reduce catastrophic forget-ting efficiently in continual learning. However, existing approaches often require a task identifier at test-time, need complex tuning to balance the growing number of parameters, and barely share any information across tasks. As a result, they struggle to scale to a large number of tasks without significant overhead. In this paper, we propose a transformer architecture based on a dedicated encoder/decoder framework. Critically, the encoder and decoder are shared among all tasks. Through a dynamic expansion of special tokens, we specialize each forward of our decoder network on a task distribution. Our strategy scales to a large number of tasks while having neg-ligible memory and time overheads due to strict control of the expansion of the parameters. Moreover, this efficient strategy doesn't need any hyperparameter tuning to control the network's expansion. Our model reaches excellent results on CIFAR100 and state-of-the-art performances on the large-scale ImageNet100 and ImageNet100 while having fewer parameters than concurrent dynamic frameworks. 1 1 Code is released at https://github.com/arthurdouillard/dytox.
Via a systematic review of the literature on learning games, this article presents a systematic discussion on the design of intrinsic integration of domain-specific learning in game mechanics and ...game world design. A total of 69 articles ultimately met the inclusion criteria and were coded for the literature synthesis. Exemplary learning games cited in the articles reviewed and developed by credible institutions were also analyzed. The cumulative findings and propositions of the game-based learning-play integration have been extracted and synthesized into five salient themes to clarify what, how, where, and when learning and content are embedded in and activated by gameplay. These themes highlight: (a) the types of game-based learning action—prior-knowledge activation and novel-knowledge acquisition, (b) the modes in which learning actions are integrated in game actions—representation, simulation, and contextualization, (c) the blended learning spaces contrived by game mechanics and the game world, (d) the occurrence of meta-reflective and iterative learning moments during game play, and (e) the multifaceted in-game learning support (or scaffolding). Future directions for the design and research of learning integration in digital games are then proposed.
Full text
Available for:
BFBNIB, DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NMLJ, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, SIK, UILJ, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ, ZRSKP
The biosignals consist of several sensors that collect time series information. Since time series contain temporal dependencies, they are difficult to process by existing machine learning algorithms. ...Hyper-Dimensional Computing (HDC) is introduced as a brain-inspired paradigm for lightweight time series classification. However, there are the following drawbacks with existing HDC algorithms: (1) low classification accuracy that comes from linear hyperdimensional representation, (2) lack of real-time learning support due to costly and non-hardware friendly operations, and (3) unable to build up a strong model from partially labeled data.
In this paper, we propose TempHD, a novel hyperdimensional computing method for efficient and accurate biosignal classification. We first develop a novel non-linear hyperdimensional encoding that maps data points into high-dimensional space. Unlike existing HDC solutions that use costly mathematics for encoding, TempHD preserves spatial-temporal information of data in original space before mapping data into high-dimensional space. To obtain the most informative representation, our encoding method considers the non-linear interactions between both spatial sensors and temporally sampled data. Our evaluation shows that TempHD provides higher classification accuracy, significantly higher computation efficiency, and, more importantly, the capability to learn from partially labeled data. We evaluate TempHD effectiveness on noisy EEG data used for a brain-machine interface. Our results show that TempHD achieves, on average, 2.3% higher classification accuracy as well as 7.7× and 21.8× speedup for training and testing time compared to state-of-the-art HDC algorithms, respectively.
In this paper, we present a design research study in Mobile Assisted Language Learning (MALL) that emphasizes learner created content and contextualized meaning making. In learning Chinese idioms, ...students proactively used smartphones on a 1:1 basis to capture photos of the real-life contexts pertaining to the idioms, and to construct sentences with them. Subsequently, in-class or online sharing and discussions on the contexts took place, which would enhance the students' understanding of the proper usage of the idioms. The learning design is grounded in seamless learning that encompasses in-class formal learning and out-of-class informal settings, and personal and social learning spaces. Our analysis of the student artifacts in both product- and process-oriented aspects reveal the students' cognitive process and learning strategies during the course of content creation. The students' ongoing, open-ended, personal-to-social meaning making process and artifacts have shown some indicators ofseamless language learningthat has the potential of transforming language learning into an authentic learning experience.
Full text
Available for:
BFBNIB, DOBA, IZUM, KILJ, NMLJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
We propose that the design and implementation of effectiveSocial Learning Analytics (SLA)present significant challenges and opportunities for both research and enterprise, in three important ...respects. The first is that the learning landscape is extraordinarily turbulent at present, in no small part due to technological drivers. Online social learning is emerging as a significant phenomenon for a variety of reasons, which we review, in order to motivate the concept of social learning. The second challenge is to identify different types of SLA and their associated technologies and uses. We discuss five categories of analytic in relation to online social learning; these analytics are either inherently social or can be socialised. This sets the scene for a third challenge, that of implementing analytics that have pedagogical and ethical integrity in a context where power and control over data are now of primary importance. We consider some of the concerns that learning analytics provoke, and suggest that Social Learning Analytics may provide ways forward. We conclude by revisiting the drivers and trends, and consider future scenarios that we may see unfold as SLA tools and services mature.
Full text
Available for:
BFBNIB, DOBA, IZUM, KILJ, NMLJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Western and East Asian people hold fundamentally different beliefs about learning that influence how they approach child rearing and education. Reviewing decades of research, Dr Jin Li presents an ...important conceptual distinction between the Western mind model and the East Asian virtue model of learning. The former aims to cultivate the mind to understand the world, whereas the latter prioritizes the self to be perfected morally and socially. Tracing the cultural origins of the two large intellectual traditions, Li details how each model manifests itself in the psychology of the learning process, learning affect, regard of one's learning peers, expression of what one knows and parents' guiding efforts. Despite today's accelerated cultural exchange, these learning models do not diminish but endure.
Background
Given the advancement of mobile and sensing technology, the incorporation of augmented reality (AR) in context‐aware ubiquitous learning (CAUL) has offered significant benefits to oral ...communication development in foreign language learning. Although a great number of studies have been dedicated in this field, only a little research was conducted to formulate a facilitation framework aligning with established learning theories or pedagogies.
Objectives
This review thereby examined the facilitation tenets conducive to the best practice of AR‐based CAUL projects for oral communication enhancement and further established a AR‐guided CAUL framework.
Methods
The researchers systematically reviewed 17 related empirical AR studies from Web of Science from 2000 to 2021 and synthesized the results with focused second language acquisition theories.
Results and Conclusions
Three practical tenets were identified, including facilitating learner‐centred pedagogies with personal and contextual learning supports, incorporating constructivist learning design, and encouraging collaborative learning and higher‐order cognitive skills. Furthermore, in accordance with the synthesized results, a facilitation framework combining situated learning, learning‐by‐doing, and social constructivist design was developed to engage students in a learning, applying, and reflecting process in formal and informal learning contexts.
Major Takeaways
For the best practice of oral communication through AR, this review study offers practical facilitation tenets and theoretical framework which are in alignment with research‐proven theories. It is expected that the framework could provide frontline educational practitioners with guidance to foster students' learning outcomes and interactions.
Lay Description
What is already known about this topic?
Foreign language learning has become interactive, contextual, and ubiquitous due to the development of mobile computing and sensing technology
The incorporation of augmented reality (AR) an context aware ubiquitous learning (CAUL) is beneficial for the facilitation of oral communication in foreign language learning
What this paper adds?
This paper offers practical facilitation tenets for oral communication development through AR‐based CAUL applications
A theoretical framework is created for the best practices of AR and CAUL‐based listening and speaking projects
Implications for practice and/or policy
The facilitation framework provides clear steps and details of implementation with learning theories to guide frontline educational practitioners and learners
This paper can assist interested stakeholders to develop learning designs and effectiveness with the combination of theoretical foundation and pedagogical applications
Full text
Available for:
BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Recent years have witnessed an increasing interest in the flipped classroom model, and many flipped programs have been funded and implemented to explore the effectiveness of this new model. However, ...previous studies centering on comparative assessment have indicated that it is not always entirely successful in terms of promoting students' performance and/or satisfaction, which warrants further research on the contributing factors and driving mechanism accounting for students' perceptions of flipped settings. In order to fill this gap, in this study, a students' satisfaction model for the flipped classroom was constructed based on the experiential learning theory. A total of 178 undergraduate students in Mainland China participated in 32-week College English flipped classes, from whom 146 valid questionnaires were obtained. The proposed research model was evaluated through longitudinal surveys followed by the structural equation modeling technique. The results indicated that, compared with the designs of Personalized Learning Climate, learners' Prior Learning Experience is a far more significant antecedent for predicting their satisfaction. Furthermore, Perceived Quality (with five first-order dimensions) and Perceived Value are two vital mediators to student satisfaction. The implications of this study are also discussed.
Full text
Available for:
BFBNIB, DOBA, IZUM, KILJ, NMLJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
During the past twenty-five years, researchers have made impressive advances in pinpointing effective learning strategies (namely, activities the learner engages in during learning that are intended ...to improve learning). In Learning as a Generative Activity: Eight Learning Strategies that Promote Understanding, Logan Fiorella and Richard E. Mayer share eight evidence-based learning strategies that promote understanding: summarizing, mapping, drawing, imagining, self-testing, self-explaining, teaching, and enacting. Each chapter describes and exemplifies a learning strategy, examines the underlying cognitive theory, evaluates strategy effectiveness by analyzing the latest research, pinpoints boundary conditions, and explores practical implications and future directions. Each learning strategy targets generative learning, in which learners actively make sense out of the material so they can apply their learning to new situations. This concise, accessible introduction to learning strategies will benefit students, researchers, and practitioners in educational psychology, as well as general readers interested in the important twenty-first-century skill of regulating one's own learning.
PDF
