Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.5
2.6
Journals
Education Sciences
Special Issues
Innovative Approaches to Understanding Student Learning
share announcement

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 4633

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

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (6 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

24 pages, 1456 KiB  
Article
Capturing the Complex: An Intraindividual Temporal Network Analysis of Learning Resource Regulation
by Bettina Harder, Nick Naujoks-Schober and Manuel D. S. Hopp
Educ. Sci. 2025, 15(6), 728; https://doi.org/10.3390/educsci15060728 - 10 Jun 2025
Viewed by 267
Abstract
Understanding a learner’s resources as a system of interacting components, the success of a learning process is determined by the effectiveness of their interactions. Theoretical assumptions and empirical findings clearly show the importance of resource availability in learning systems but do not sufficiently [...] Read more.
Understanding a learner’s resources as a system of interacting components, the success of a learning process is determined by the effectiveness of their interactions. Theoretical assumptions and empirical findings clearly show the importance of resource availability in learning systems but do not sufficiently consider the individuality or the temporal and situational aspects of resource regulation. Therefore, the current study addresses the complex interplay between learning resources (educational and learning capitals) in an individual learner (N = 1) by utilizing multivariate time series data of a 50-day vocabulary learning process with daily assessments of learning resource availability, performance, learning duration, and stress. We draw on methods of psychometric network analysis, modeling all variables in simultaneous interaction and allowing predictions between all variables from measuring point to measuring point (temporal dynamics). Specifically, using a Graphical Vector Autoregressive (graphicalVAR) model, yielding a contemporaneous and a temporal dynamics network model, we identified pivotal resources in regulating the student’s learning processes and outcomes, including resources with strong connections to other variables, intermediary resources, and resources maintaining the system’s homeostasis. This innovative approach has possible applications as a diagnostic tool that lays the foundation for tailored interventions. Full article
(This article belongs to the Special Issue Innovative Approaches to Understanding Student Learning)
►▼ Show Figures

Figure 1

12 pages, 206 KiB  
Article
Between Surviving and Thriving—New Approaches to Understanding Learning for Transformation
by Saskia Eschenbacher
Educ. Sci. 2025, 15(6), 662; https://doi.org/10.3390/educsci15060662 - 27 May 2025
Viewed by 269
Abstract
Background: Paramedics and firefighters frequently encounter critical incidents that require both deep learning and emotional processing. This study investigates how reflective writing facilitates these processes, addressing the need to understand professional development in high-stress environments. Methods: The research analyzed reflective writings from 57 [...] Read more.
Background: Paramedics and firefighters frequently encounter critical incidents that require both deep learning and emotional processing. This study investigates how reflective writing facilitates these processes, addressing the need to understand professional development in high-stress environments. Methods: The research analyzed reflective writings from 57 second-year Management of Catastrophe Defense undergraduates who were active emergency service workers. Using Mayring’s qualitative content analysis, the study examined participants’ descriptions of critical workplace incidents, emotional responses, and long-term impacts. The theoretical framework combines Paul’s concept of transformative experiences, Schön’s reflective practice, and Jarvis’s experiential learning theory. Results: The analysis revealed three key dimensions: transformative experiences, the role of conversation with the situation in meaning making and the significance of whole-person learning in understanding emotional presence and absence, and the role of reflective writing in understanding learning processes. The study uncovered complex patterns in how professionals process critical incidents and manage emotions in high-stress environments. Conclusions: Reflective writing serves as an effective tool for processing experiences and developing professional resilience, although the process of engaging with traumatic memories through reflection presents its own complexities. These insights contribute to the understanding of learning processes and professional development in high-stress environments. Full article
(This article belongs to the Special Issue Innovative Approaches to Understanding Student Learning)
20 pages, 817 KiB  
Article
Measuring Personalized Learning in the Smart Classroom Learning Environment: Development and Validation of an Instrument
by Pan Tuo, Mehmet Bicakci, Albert Ziegler and BaoHui Zhang
Educ. Sci. 2025, 15(5), 620; https://doi.org/10.3390/educsci15050620 - 19 May 2025
Viewed by 415
Abstract
Smart classrooms leverage intelligent and mobile technologies to create highly interactive, student-centered environments conducive to personalized learning. However, measuring students’ personalized learning experiences in these technologically advanced spaces remains a challenge. This study addresses the gap by developing and validating a Smart Classroom [...] Read more.
Smart classrooms leverage intelligent and mobile technologies to create highly interactive, student-centered environments conducive to personalized learning. However, measuring students’ personalized learning experiences in these technologically advanced spaces remains a challenge. This study addresses the gap by developing and validating a Smart Classroom Environment–Personalized Learning Scale (SCE-PL). Drawing on a comprehensive literature review, content-expert feedback, and iterative item refinement, an initial pool of 48 items was reduced to 39 and subsequently to 34 following item-level analyses. Two datasets were collected from Chinese middle-school students across three provinces, capturing diverse socio-economic contexts and grade levels (7th, 8th, and 9th). EFA on the first dataset (n = 424) revealed a nine-factor structure collectively explaining 78.12% of the total variance. Confirmatory factor analysis (CFA) on the second dataset (n = 584) verified an excellent model fit. Internal consistency indices (Cronbach’s α > 0.87, composite reliability > 0.75) and strong convergent and discriminant validity evidence (based on AVE and inter-factor correlations) further support the scale’s psychometric soundness. The SCE-PL thus offers researchers, policymakers, and practitioners a robust, theory-driven instrument for assessing personalized learning experiences in smart classroom environments, paving the way for data-informed pedagogy, optimized learning spaces, and enhanced technological integration. Full article
(This article belongs to the Special Issue Innovative Approaches to Understanding Student Learning)
►▼ Show Figures

Figure 1

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 378
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)
►▼ Show Figures

Figure 1

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
Cited by 1 | Viewed by 842
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)
►▼ Show Figures

Figure 1

Review

Jump to: Research

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 1621
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)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-6
Back to TopTop