Creating Microlearning Objects Within Self-Directed Multimodal Learning Contexts

2021 ◽  
pp. 169-188
Author(s):  
Jako Olivier
Keyword(s):  
Multimodal Learning ◽  
Learning Contexts

scholarly journals MOLAM: A Mobile Multimodal Learning Analytics Conceptual Framework to Support Student Self-Regulated Learning

2020 ◽  
Author(s):  
Mohammad Khalil
Keyword(s):  
Online Learning ◽  
Conceptual Framework ◽  
Learning Analytics ◽  
Continuous Measurement ◽  
Multimodal Learning ◽  
Self Regulated Learning ◽  
Learning Contexts ◽  
Regulated Learning ◽  
Multimodal Learning Analytics

MOLAM is a Mobile Multimodal Learning Analytics Conceptual Framework to Support Student Self-Regulated Learning. This chapter introduces a Mobile Multimodal Learning Analytics approach (MOLAM). I argue that the development of student SRL would benefit from the adoption of this approach and that its use would allow continuous measurement and provision of in-time support of student SRL in online learning contexts.

Theoretical and Methodological Elements of an Inclusive Approach to Education

2020 ◽  
pp. 123-136
Author(s):  
Antonello Mura ◽  
Antioco Luigi Zurru ◽  
Ilaria Tatulli
Keyword(s):  
Inclusive Education ◽  
Collaborative Work ◽  
Qualitative Investigation ◽  
Training Process ◽  
Continuous Training ◽  
Learning Contexts ◽  
Inclusive Learning ◽  
Inclusive Approach ◽  
Slow Evolution ◽  
Methodological Principles

The educative experience of people with disability leads the inter­na­tio­nal debate towards the value of inclusive learning contexts. Nonetheless, the theoretical and methodological principles of an inclusive education approach have to be outlined. Data collected using explorative questionnaires during a five-years survey in an Italian region's schools show a slow evolution of the scholastic context. From the perspective of Special Pedagogy, the qualitative investigation on three macro-dimensions (the diversity perception, the didactic and methodological means, the wellbeing of pupils) reveals an emerging development of solid awareness among teachers. Findings confirm that the inclusion processes at school are attainable only throughout a series of clear methodological elements: 1) a valorising attitude towards diversity; 2) an orienting learning process; 3) a plural and flexible use of both methodologies and strategies; 4) a collaborative work environment; 5) a continuous training process; 6) a deontological approach. These are the principles that allow teachers to support each student in the manifold itineraries of identity fulfilment, encouraging pupils to express their needs and to develop their abilities in a welcoming and participative context.

The Primacy of Experience

2019 ◽  
pp. 64-81
Author(s):  
Andrea Schiavio
Keyword(s):  
Ecological Factors ◽  
Assessment Practices ◽  
Flexible Systems ◽  
Phenomenological Philosophy ◽  
The Core ◽  
Learning Contexts ◽  
Grading Systems ◽  
Recent Developments ◽  
Core Issue ◽  
Event Based

This chapter explores a possible alternative to traditional “paper-and-pencil” assessment practices in music classes. It argues that an approach based on phenomenological philosophy and inspired by recent developments in cognitive science may shed new light on learning and help educators reconsider grading systems accordingly. After individuating the core issue in an unresolved tension between subjective-objective methodologies relevant to certain learning contexts, the chapter proposes a possible remedy by appealing to three principles central to “embodied” approaches to cognition. Such principles may help educators reframe cognitive phenomena (learning described as a measurable event based on “information processing”) in terms of cognitive ecosystems (learning understood as a negotiating and transformative activity codetermined by diverse embodied and ecological factors connected in recurrent fashion). Accommodating this shift implies transforming assessment practices into more open and flexible systems that take seriously the challenge of cooperative learning and phenomenological reflections.

scholarly journals Predicting TCR-Epitope Binding Specificity Using Deep Metric Learning and Multimodal Learning

Genes ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 572
Author(s):  
Alan M. Luu ◽  
Jacob R. Leistico ◽  
Tim Miller ◽  
Somang Kim ◽  
Jun S. Song
Keyword(s):  
Neural Network ◽  
Amino Acid ◽  
Cytotoxic T Cells ◽  
Metric Learning ◽  
Binding Specificity ◽  
Class I ◽  
Multimodal Learning ◽  
Binding Prediction ◽  
Deep Metric Learning ◽  
Epitope Binding

Understanding the recognition of specific epitopes by cytotoxic T cells is a central problem in immunology. Although predicting binding between peptides and the class I Major Histocompatibility Complex (MHC) has had success, predicting interactions between T cell receptors (TCRs) and MHC class I-peptide complexes (pMHC) remains elusive. This paper utilizes a convolutional neural network model employing deep metric learning and multimodal learning to perform two critical tasks in TCR-epitope binding prediction: identifying the TCRs that bind a given epitope from a TCR repertoire, and identifying the binding epitope of a given TCR from a list of candidate epitopes. Our model can perform both tasks simultaneously and reveals that inconsistent preprocessing of TCR sequences can confound binding prediction. Applying a neural network interpretation method identifies key amino acid sequence patterns and positions within the TCR, important for binding specificity. Contrary to common assumption, known crystal structures of TCR-pMHC complexes show that the predicted salient amino acid positions are not necessarily the closest to the epitopes, implying that physical proximity may not be a good proxy for importance in determining TCR-epitope specificity. Our work thus provides an insight into the learned predictive features of TCR-epitope binding specificity and advances the associated classification tasks.

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