Experimental Psychology Its Scope and Method. IV. Learning and Memory. By Jean-Francois Le Ny, G. De Montpellier, G. Oleron and Cesar Flores. Translated by Louise Elkington. Edited by P. Fraisse and Jean Piaget. Pp. viii + 376. (Routledge and Kegan Paul: London, April 1970.) 80s.

Learning and Memory

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Finding Flow The Psychology Of Engagement With Everyday Life

In the 21st century, the information and communication technology explosion increases the uses of digital devices for many purposes in the world of work and in formal and non-formal education. This...

https://journals.sagepub.com/doi/pdf/10.1177/2042753018785180

E-learning, M-learning and D-learning: Conceptual definition and comparative analysis:

Although legislation mandates that students with autism receive instruction linked to the general education core content, there is limited research supporting the effectiveness of interventions for teaching core content to these students. In this study, the author reviewed research conducted between the years 1997 and 2008 using computer-assisted instruction (CAI) to teach academic skills to students with autism. The author concluded that CAI was effective for teaching a limited set of academic skills to individuals with autism; however, functional relations were found in few of the single-case designs and none of the group designs included a control group. Future researchers should explore the use of CAI in various instructional arrangements, identify critical technology components, and evaluate commercially available software.

Computer-Assisted Instruction for Teaching Academic Skills to Students With Autism Spectrum Disorders: A Review of Literature

Learning multimodal representations is a fundamentally complex research problem due to the presence of multiple heterogeneous sources of information. Although the presence of multiple modalities provides additional valuable information, there are two key challenges to address when learning from multimodal data: 1) models must learn the complex intra-modal and cross-modal interactions for prediction and 2) models must be robust to unexpected missing or noisy modalities during testing. In this paper, we propose to optimize for a joint generative-discriminative objective across multimodal data and labels. We introduce a model that factorizes representations into two sets of independent factors: multimodal discriminative and modality-specific generative factors. Multimodal discriminative factors are shared across all modalities and contain joint multimodal features required for discriminative tasks such as sentiment prediction. Modality-specific generative factors are unique for each modality and contain the information required for generating data. Experimental results show that our model is able to learn meaningful multimodal representations that achieve state-of-the-art or competitive performance on six multimodal datasets. Our model demonstrates flexible generative capabilities by conditioning on independent factors and can reconstruct missing modalities without significantly impacting performance. Lastly, we interpret our factorized representations to understand the interactions that influence multimodal learning.

Learning Factorized Multimodal Representations

There is a general belief that software must be able to easily do things that humans find difficult. Since finding sources for plagiarism in a text is not an easy task, there is a wide-spread expectation that it must be simple for software to determine if a text is plagiarized or not. Software cannot determine plagiarism, but it can work as a support tool for identifying some text similarity that may constitute plagiarism. But how well do the various systems work? This paper reports on a collaborative test of 15 web-based text-matching systems that can be used when plagiarism is suspected. It was conducted by researchers from seven countries using test material in eight different languages, evaluating the effectiveness of the systems on single-source and multi-source documents. A usability examination was also performed. The sobering results show that although some systems can indeed help identify some plagiarized content, they clearly do not find all plagiarism and at times also identify non-plagiarized material as problematic.

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Testing of support tools for plagiarism detection

Emotion Recognition in Affective Tutoring Systems

The broad terminology describing possible learning ways and approaches that use various technologies in the learning process has appeared together with rapid growth of information and communication technologies. This terminology should be ordered and precisely defined. This article presents eight groups of the most widespread terms (such as computer-based learning, distance learning, e-learning, Internetbased learning, online learning, resource-based learning, technology-based learning, Web-based learning) and defines subset relationships among the groups. In addition the meaning of some terms coming from pedagogy (education, instruction, learning, teaching, training, and tutoring) and used as obligatory constituent part of other terms is clarified.

Analysis of the Terminology Used in the Field of Virtual Learning

Agents in Intelligent Tutoring Systems: State of the Art

The paper is devoted to the concept map-based intelligent assessment system that promotes students' knowledge self-assessment and supports teachers in improvement of study courses through systematic assessment and analysis of students' knowledge on the basis of concept maps. During the last five years, both the system's functionality and knowledge assessment approach were improved persistently, and at the moment, certain level of maturity is reached in both directions. The paper focuses on general principles of functioning of the last prototype, tasks provided, teachers' and students' support, scoring and adaptation mechanisms. At the end of the paper, retrospection of the system's evolution and evaluation results is provided.

Alla Anohina-Naumeca

Papers

Testing of support tools for plagiarism detection

Emotion Recognition in Affective Tutoring Systems

Analysis of the Terminology Used in the Field of Virtual Learning

Agents in Intelligent Tutoring Systems: State of the Art

The Concept Map Based Assessment System: Functional Capabilities, Evolution and Experimental Results