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Education Sciences
Special Issues
Innovative Approaches to Understanding Student Learning
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Innovative Approaches to Understanding Student Learning

A special issue of Education Sciences (ISSN 2227-7102).

Deadline for manuscript submissions: closed (31 January 2025) | Viewed by 2762

Special Issue Editors

Dr. Bettina Harder

E-Mail Website
Guest Editor
Department of Psychology, Friedrich-Alexander-Universität Erlangen-Nürnberg, 90478 Nürnberg, Germany
Interests: systemic interplay and regulation; learning processes; talent development; educational diagnostics
Dr. Nick Naujoks-Schober

E-Mail Website
Guest Editor
Department of Psychology, Friedrich-Alexander-Universität Erlangen-Nürnberg, 90478 Nürnberg, Germany
Interests: self-regulated learning; metacognition; testing; higher education

Special Issue Information

Dear Colleagues, 

We are pleased to announce our forthcoming Special Issue titled "Innovative Approaches to Understanding Student Learning" in Education Sciences. Presently, student learning from primary to higher education settings encounters numerous challenges, including—but not limited to—integrating digital technologies, managing diversity, equity, and inclusion; adapting to globalization; navigating the repercussions of the COVID-19 pandemic; and addressing the intricate interplay of factors that influence learning outcomes. These challenges underscore the necessity for pioneering research to deepen our comprehension of learning processes within these dynamically evolving contexts.

The primary objective of this Special Issue is to curate original research and reviews that offer insights into innovative theoretical frameworks about student learning or novel methodological approaches encompassing study designs, assessment tools, analytical methodologies, and diverse participant samples.

We welcome submissions that adopt such progressive approaches across all facets of learning, such as exploring determinants of academic success, understanding competency acquisition, elucidating learning processes, and investigating the various resources that facilitate learning. Furthermore, we encourage submissions that showcase innovative research on student learning across diverse educational settings, including investigations into the efficacy of instructional methods, the impact of digital and artificial intelligence-supported learning environments, examinations of self-regulated and co-regulated learning conditions, and analyses of curricular frameworks and institutional structures. 

We look forward to receiving your contributions. 

Dr. Bettina Harder
Dr. Nick Naujoks-Schober
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 double-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Education Sciences is an international peer-reviewed open access monthly 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 1800 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

  • learning process
  • academic achievement
  • competency acquisition
  • learning environment
  • instruction
  • regulation of learning
  • assessment
  • learning resources

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

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42 pages, 3877 KiB  
Article
Modelling the Interactions Between Resources and Academic Achievement: An Artificial Neural Network Approach
by Cindy Di Han, Shane N. Phillipson and Vincent C S Lee
Educ. Sci. 2025, 15(5), 519; https://doi.org/10.3390/educsci15050519 - 22 Apr 2025
Viewed by 203
Abstract
The actiotope model of giftedness takes a systems approach to understand the development of exceptionality and, more broadly, the academic achievement of students. Focusing primarily on the interactions between environmental capitals and outcomes such as academic achievement, research has relied on methods such [...] Read more.
The actiotope model of giftedness takes a systems approach to understand the development of exceptionality and, more broadly, the academic achievement of students. Focusing primarily on the interactions between environmental capitals and outcomes such as academic achievement, research has relied on methods such as structural equation modelling (SEM) to understand these interactions. However, such methods do not reflect the nonlinear interactions inherent within systems. Based on datasets obtained from students from one Australian school (n = 778), both SEM and artificial neural networks (ANNs) were created for school-assessed achievement scores (mathematics, english and science) and standardised test scores (mathematics, vocabulary, and reading). Using the optimal ANN for school-assessed achievement scores for mathematics, its potential to predict future scores based on hypothetical improvements to five of the 11 capitals was confirmed. With high quality data, the use of ANNs will allow researchers to better understand these interactions and support practitioners to implement evidence-based interventions. Full article
(This article belongs to the Special Issue Innovative Approaches to Understanding Student Learning)
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15 pages, 451 KiB  
Article
Same Classroom, Different Reality: Secondary School Students’ Perceptions of STEM Lessons—A Pioneering Study
by Lukas Ketscher, Heidrun Stoeger, Wilma Vialle and Albert Ziegler
Educ. Sci. 2025, 15(4), 467; https://doi.org/10.3390/educsci15040467 - 8 Apr 2025
Viewed by 575
Abstract
Our study is the first exploration of students’ situational perceptions of STEM lessons based on the DIAMONDS approach. This approach postulates eight perceptual dimensions: Duty, Intellect, Adversity, Mating, pOsitivity, Negativity, Deception, and Sociality. Three research [...] Read more.
Our study is the first exploration of students’ situational perceptions of STEM lessons based on the DIAMONDS approach. This approach postulates eight perceptual dimensions: Duty, Intellect, Adversity, Mating, pOsitivity, Negativity, Deception, and Sociality. Three research questions were investigated in a validation study involving 447 eighth graders, each based on a distinct validation strategy. (1) Convergent validation strategy: How do students perceive STEM lessons regarding the DIAMONDS dimensions? (2) Criterion-related validation strategy: Are these perceptions associated with STEM education outcomes? (3) Explanatory validation strategy: Do gender differences also appear in the perception of STEM lessons? Data were collected via an online questionnaire. The main results indicated that (1) students associate STEM lessons mainly with Duty and Intellect; (2) their situational perception of STEM lessons was linked to STEM education outcomes; and (3) there were substantial variances in how students perceive STEM lessons. Male students perceived STEM lessons more positively (pOsitivity), while females associated them relatively more with negative attributes (Adversity, Negativity, or Deception). All three validation strategies produced results confirming the validity of the DIAMONDS approach. In this way, the results of our study offer a promising start for the DIAMONDS approach in STEM education research. Full article
(This article belongs to the Special Issue Innovative Approaches to Understanding Student Learning)
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Review

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25 pages, 652 KiB  
Review
Learning Maps as Cognitive Models for Instruction and Assessment
by Russell Swinburne Romine, Jonathan Schuster, Meagan Karvonen, W. Jake Thompson, Karen Erickson, Vanessa Simmering and Sue Bechard
Educ. Sci. 2025, 15(3), 365; https://doi.org/10.3390/educsci15030365 - 14 Mar 2025
Viewed by 1236
Abstract
This paper describes large, fine-grained, intentionally inclusive, research-based cognitive models called learning maps. Learning maps are the product of an intensive research synthesis process to develop formal learning models that better reflect the diversity of how learners can learn and be assessed on [...] Read more.
This paper describes large, fine-grained, intentionally inclusive, research-based cognitive models called learning maps. Learning maps are the product of an intensive research synthesis process to develop formal learning models that better reflect the diversity of how learners can learn and be assessed on academic content. Students begin at different places and learn at different rates, and they may have cognitive disabilities or may face a variety of barriers that pose challenges when learning content. Learning maps provide numerous starting points and pathways by which students can acquire and demonstrate knowledge, skills, and understandings. Our work in developing learning maps relies on principles of Universal Design for Learning (UDL), which provides a foundation of flexibility and inclusivity to accommodate students with a wide range of cognitive, linguistic, physical, and sensory profiles. In this paper, we describe learning map design, development, and both qualitative and quantitative methods for the evaluation of map structure. In addition, we offer reflections on our experiences with implementing learning maps as the cognitive architecture for assessments and educational interventions through our work on a variety of projects. With examples from these projects, we describe evidence that shows how learning maps can be useful tools for improving instruction and assessment for all learners. We identify areas where further research and inquiry could prove fruitful and conclude with a discussion of potential areas of extension and offer suggestions for the ongoing refinement of learning maps. Full article
(This article belongs to the Special Issue Innovative Approaches to Understanding Student Learning)
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