scholarly journals The Continuous Pursuit of Smart Learning

2021 ◽  
Vol 37 (2) ◽  
pp. 1-6
Author(s):  
Simon K. S. Cheung ◽  
Fu Lee Wang ◽  
Lam For Kwok
Keyword(s):  
Adaptive Learning ◽  
Learning Performance ◽  
Pattern Learning ◽  
Special Issue ◽  
Smart Learning ◽  
Personalised Learning ◽  
New Concepts ◽  
Learning Flexibility ◽  
Evaluation Of Learning ◽  
Assessment Strategies

With an emphasis on learning flexibility, effectiveness, efficiency, engagement, adaptivity and reflectiveness, smart learning embraces a variety of concepts, including but not limited to personalised learning, adaptive learning, intelligent tutoring, open online learning, blended learning, and collaborative learning. As new concepts continue to evolve, the pursuit of smart learning is ongoing, mainly in areas pertaining to the design and implementation frameworks, pedagogical theories and practices, learners’ behaviours and learning pattern, learning and assessment strategies and evaluation of learning performance and perception. This editorial gives an overview of smart learning and provides the context on the latest development of smart learning in which the articles in this special issue are located.

Preface to the special issue: adaptive learning agents

2016 ◽  
Vol 31 (1) ◽  
pp. 1-2
Author(s):  
Peter Vrancx ◽  
Enda Howley ◽  
Matt Knudson
Keyword(s):  
Adaptive Learning ◽  
Special Issue ◽  
Learning Agents

Open Learner Models in Smart Learning Environments

2019 ◽  
pp. 346-368 ◽  
Author(s):  
Angeliki Leonardou ◽  
Maria Rigou ◽  
John D. Garofalakis
Keyword(s):  
Learning Environments ◽  
Adaptive Learning ◽  
Real Life ◽  
Self Reflection ◽  
Learner Model ◽  
Self Regulated Learning ◽  
Smart Learning ◽  
Smart Learning Environments ◽  
Navigation Support ◽  
Adaptive Learning Systems

Smart learning environments (SLEs), like all adaptive learning systems, are built around the learner model and use it to support a variety of interventions such as mastery learning, scaffolding, adaptive sequencing, and adaptive navigation support. Open learner models (OLMs) “expose” the learner data to users through easily perceivable visual representations aiming to improve student self-reflection and self-regulated learning and also increase user motivation and even foster collaboration. This chapter presents the evolution and current state of OLMs, summarizes related research in the field emphasizing on OLM types, locus of control between the system and the user and visualizations categorized on the basis of quantized/continuous and structured/unstructured representations. OLM cases implementing typical SLEs features are described, along with representative real-life scenarios of incorporating OLMs in SLEs. Moreover, the chapter provides guidelines for designing effective OLMs and discusses current research trends in this active scientific field.

A Cross-Correlated Delay Shift Supervised Learning Method for Spiking Neurons with Application to Interictal Spike Detection in Epilepsy

2017 ◽  
Vol 27 (03) ◽  
pp. 1750002 ◽  
Author(s):  
Lilin Guo ◽  
Zhenzhong Wang ◽  
Mercedes Cabrerizo ◽  
Malek Adjouadi
Keyword(s):  
Adaptive Learning ◽  
Learning Algorithm ◽  
Recognition Performance ◽  
Learning Rule ◽  
Classification Performance ◽  
Spiking Neurons ◽  
Learning Performance ◽  
Learning Method ◽  
Neuron Models ◽  
Interictal Spike

This study introduces a novel learning algorithm for spiking neurons, called CCDS, which is able to learn and reproduce arbitrary spike patterns in a supervised fashion allowing the processing of spatiotemporal information encoded in the precise timing of spikes. Unlike the Remote Supervised Method (ReSuMe), synapse delays and axonal delays in CCDS are variants which are modulated together with weights during learning. The CCDS rule is both biologically plausible and computationally efficient. The properties of this learning rule are investigated extensively through experimental evaluations in terms of reliability, adaptive learning performance, generality to different neuron models, learning in the presence of noise, effects of its learning parameters and classification performance. Results presented show that the CCDS learning method achieves learning accuracy and learning speed comparable with ReSuMe, but improves classification accuracy when compared to both the Spike Pattern Association Neuron (SPAN) learning rule and the Tempotron learning rule. The merit of CCDS rule is further validated on a practical example involving the automated detection of interictal spikes in EEG records of patients with epilepsy. Results again show that with proper encoding, the CCDS rule achieves good recognition performance.

scholarly journals Alleviating the Cold Start Problem in Adaptive Learning using Data-Driven Difficulty Estimates

2020 ◽  
Author(s):  
Maarten van der Velde ◽  
Florian Sense ◽  
Jelmer P Borst ◽  
Hedderik van Rijn
Keyword(s):  
Learning Outcomes ◽  
Adaptive Learning ◽  
Internal Model ◽  
Cold Start ◽  
Learning System ◽  
Learning Performance ◽  
Learning Material ◽  
Actual Performance ◽  
Individual Learner ◽  
Cold Start Problem

An adaptive learning system offers a digital learning environment that adjusts itself to the individual learner and learning material. By refining its internal model of the learner and material over time, such a system continually improves its ability to present appropriate exercises that maximise learning gains. In many cases, there is an initial mismatch between the internal model and the learner’s actual performance on the presented items, causing a ‘cold start’ during which the system is poorly adjusted to the situation. In this study, we implemented several strategies for mitigating this cold start problem in an adaptive fact learning system and experimentally tested their effect on learning performance. The strategies included predicting difficulty for individual learner-fact pairs, individual learners, individual facts, and the set of facts as a whole. We found that cold start mitigation improved learning outcomes, provided that there was sufficient variability in the difficulty of the study material. Informed individualised predictions allowed the system to schedule learners’ study time more effectively, leading to an increase in response accuracy during the learning session as well as improved retention of the studied items afterwards. Our findings show that addressing the cold start problem in adaptive learning systems can have a real impact on learning outcomes. We expect this to be particularly valuable in real-world educational settings with large individual differences between learners and highly diverse materials.

scholarly journals Editorial: Beyond Cognitive Ability

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Srecko Joksimovic ◽  
George Siemens ◽  
Yuan Elle Wang ◽  
M. O. Z. San Pedro ◽  
Jason Way
Keyword(s):  
Peer Effects ◽  
Cognitive Architecture ◽  
Self Regulation ◽  
Learning Activities ◽  
Learning Performance ◽  
Cognitive Tasks ◽  
Memory Encoding ◽  
Special Issue ◽  
The Past ◽  
Data Collection And Analysis

The past 70 years of research in learning has primarily favoured a cognitive perspective. As such, learning and learning performance were measured based on factors such as memory, encoding, and retrieval. More sophisticated learning activities, such as perspective changes, still relied on a fundamental cognitive architecture (Dunlosky & Rawson, 2019). Early researchers advocating for a constructivist learning lens, such as Piaget, also assessed development on a range of cognitive tasks. Over the past several decades, this view of learning as cognitive has given rise to a range of augmenting perspectives. Researchers increasingly focus on mindsets, social learning, peer effects, self-regulation, and self-perception to evaluate the broader scope of learning. For learning analytics (LA), this transition has important implications for data collection and analysis, tools and technologies used, research design, and experimentation. This special issue continues existing conversations around LA and emerging competencies (Dawson & Siemens, 2014; Buckingham Shum & Crick, 2016) but also reflects the growing number of researchers engaging with these topics.

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