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Applied Sciences
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Deep Learning and Technology-Assisted Education
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Deep Learning and Technology-Assisted Education

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (15 March 2024) | Viewed by 2644

Special Issue Editors

Prof. Dr. Alberto Gil Solla

E-Mail Website
Guest Editor
atlanTTic Research Center for Telecommunication Technologies, University of Vigo, 36310 Vigo, Spain
Interests: design and development of intelligent systems for personalization of Internet and mobile applications, including automatic content recommendation, especially with natural language processing techniques and other machine learning approaches involving neural networks
Prof. Dr. Yolanda Blanco Fernández

E-Mail Website
Guest Editor
atlanTTic research Center for Telecommunication Technologies, University of Vigo, 36310 Vigo, Spain
Interests: semantic reasoning in personalization applications; machine learning techniques; deep learning models for natural language processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the ever-evolving field of education, fostering innovative approaches to enrich students’ learning experiences stands as a pivotal imperative. Throughout history, numerous initiatives have emerged in tandem with technological progress, catalyzing a profound metamorphosis in our educational paradigm. The adoption of computers, in particular, has paved the way for the evolution of diverse pedagogical models including flipped learning, self-regulated learning, and fully online education, all of which have cemented their significance, especially during and after the COVID-19 pandemic.

The surge in machine learning (ML) technologies and the enthusiastic adoption of various neural network models in deep learning (DL), including recurrent, convolutional, LSTM networks, and the groundbreaking transformer architectures, which possess the remarkable ability to generate human-level text, present challenging prospects in the field of education. These technologies hold the potential to boost the development and implementation of personalized, adaptable, and highly efficient educational experiences, finely tuned to address individual students' unique needs and levels of knowledge.

Given this technological backdrop, this Special Issue aims to explore the appealing intersection of technology-assisted education and deep learning with the overarching goal of advancing toward more captivating and effective learning experiences that invigorate students’ motivation and improve their academic performance. In this context, we invite prospective authors to submit articles covering, but not limited to, the following thematic areas related to technology-assisted education, where deep learning can serve as a valuable opportunity:

  • Personalized learning
  • Inclusive education through technology
  • Natural language processing for adaptive learning environments
  • Development of intelligent tutoring systems
  • Deep reinforcement learning as an educational tool
  • Detection of student participation and engagement in flipped learning
  • Promotion of critical thinking skills
  • Advances in lifelong learning

Prof. Dr. Alberto Gil Solla
Prof. Dr. Yolanda Blanco Fernández
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • intelligent learning environments
  • deep learning in personalized educational settings
  • creation of ad hoc learning resources
  • deep learning driven education
  • learning analytics

Published Papers (3 papers)

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Research

15 pages, 3637 KiB  
Article
The Impact of Large Language Models on Programming Education and Student Learning Outcomes
by Gregor Jošt, Viktor Taneski and Sašo Karakatič
Appl. Sci. 2024, 14(10), 4115; https://doi.org/10.3390/app14104115 - 13 May 2024
Viewed by 409
Abstract
Recent advancements in Large Language Models (LLMs) like ChatGPT and Copilot have led to their integration into various educational domains, including software development education. Regular use of LLMs in the learning process is still not well-researched; thus, this paper intends to fill this [...] Read more.
Recent advancements in Large Language Models (LLMs) like ChatGPT and Copilot have led to their integration into various educational domains, including software development education. Regular use of LLMs in the learning process is still not well-researched; thus, this paper intends to fill this gap. The paper explores the nuanced impact of informal LLM usage on undergraduate students’ learning outcomes in software development education, focusing on React applications. We carefully designed an experiment involving thirty-two participants over ten weeks where we examined unrestricted but not specifically encouraged LLM use and their correlation with student performance. Our results reveal a significant negative correlation between increased LLM reliance for critical thinking-intensive tasks such as code generation and debugging and lower final grades. Furthermore, a downward trend in final grades is observed with increased average LLM use across all tasks. However, the correlation between the use of LLMs for seeking additional explanations and final grades was not as strong, indicating that LLMs may serve better as a supplementary learning tool. These findings highlight the importance of balancing LLM integration with the cultivation of independent problem-solving skills in programming education. Full article
(This article belongs to the Special Issue Deep Learning and Technology-Assisted Education)
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19 pages, 3810 KiB  
Article
Advanced Deep Learning Model for Predicting the Academic Performances of Students in Educational Institutions
by Laith H. Baniata, Sangwoo Kang, Mohammad A. Alsharaiah and Mohammad H. Baniata
Appl. Sci. 2024, 14(5), 1963; https://doi.org/10.3390/app14051963 - 28 Feb 2024
Viewed by 994
Abstract
Educational institutions are increasingly focused on supporting students who may be facing academic challenges, aiming to enhance their educational outcomes through targeted interventions. Within this framework, leveraging advanced deep learning techniques to develop recommendation systems becomes essential. These systems are designed to identify [...] Read more.
Educational institutions are increasingly focused on supporting students who may be facing academic challenges, aiming to enhance their educational outcomes through targeted interventions. Within this framework, leveraging advanced deep learning techniques to develop recommendation systems becomes essential. These systems are designed to identify students at risk of underperforming by analyzing patterns in their historical academic data, thereby facilitating personalized support strategies. This research introduces an innovative deep learning model tailored for pinpointing students in need of academic assistance. Utilizing a Gated Recurrent Neural Network (GRU) architecture, the model is rich with features such as a dense layer, max-pooling layer, and the ADAM optimization method used to optimize performance. The effectiveness of this model was tested using a comprehensive dataset containing 15,165 records of student assessments collected across several academic institutions. A comparative analysis with existing educational recommendation models, like Recurrent Neural Network (RNN), AdaBoost, and Artificial Immune Recognition System v2, highlights the superior accuracy of the proposed GRU model, which achieved an impressive overall accuracy of 99.70%. This breakthrough underscores the model’s potential in aiding educational institutions to proactively support students, thereby mitigating the risks of underachievement and dropout. Full article
(This article belongs to the Special Issue Deep Learning and Technology-Assisted Education)
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16 pages, 1256 KiB  
Article
Investigating Models for the Transcription of Mathematical Formulas in Images
by Christian Feichter and Tim Schlippe
Appl. Sci. 2024, 14(3), 1140; https://doi.org/10.3390/app14031140 - 29 Jan 2024
Viewed by 889
Abstract
The automated transcription of mathematical formulas represents a complex challenge that is of great importance for digital processing and comprehensibility of mathematical content. Consequently, our goal was to analyze state-of-the-art approaches for the transcription of printed mathematical formulas on images into spoken English [...] Read more.
The automated transcription of mathematical formulas represents a complex challenge that is of great importance for digital processing and comprehensibility of mathematical content. Consequently, our goal was to analyze state-of-the-art approaches for the transcription of printed mathematical formulas on images into spoken English text. We focused on two approaches: (1) The combination of mathematical expression recognition (MER) models and natural language processing (NLP) models to convert formula images first into LaTeX code and then into text, and (2) the direct conversion of formula images into text using vision-language (VL) models. Since no dataset with printed mathematical formulas and corresponding English transcriptions existed, we created a new dataset, Formula2Text, for fine-tuning and evaluating our systems. Our best system for (1) combines the MER model LaTeX-OCR and the NLP model BART-Base, achieving a translation error rate of 36.14% compared with our reference transcriptions. In the task of converting LaTeX code to text, BART-Base, T5-Base, and FLAN-T5-Base even outperformed ChatGPT, GPT-3.5 Turbo, and GPT-4. For (2), the best VL model, TrOCR, achieves a translation error rate of 42.09%. This demonstrates that VL models, predominantly employed for classical image captioning tasks, possess significant potential for the transcription of mathematical formulas in images. Full article
(This article belongs to the Special Issue Deep Learning and Technology-Assisted Education)
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