Student Research Poster Award at the Gender & STEM Conference 2026
July 27, 2026
The winning poster is titled "The Female Fish in the Big Male Pond: Student and Achievement Compositions' Impact on Mathematics Self-Efficacy and STEM Career Aspirations". Drawing on PISA data, the study shows that the gender and achievement composition of schools meaningfully shapes student development: a higher proportion of boys is associated with lower mathematics self-efficacy among girls, and higher-achieving peer groups are linked to lower STEM career aspirations.
Profiles of Moral Self-Concept and Normative Stances
July 21, 2026
This study investigated the development of young children’s normative stances and moral self-concept (MSC) over one year and their relations with sharing behavior. Five hundred children in Germany (approx. 5 years at T1, mostly White, 51% girls) were assessed at three measurement points. Latent profile and transition analyses identified seven distinct profiles of normative stances—MSC: 1) low—moderate, 2) moderate—exceptionally low, 3) high—moderate, 4) moderate—low, 5) moderate—high, 6) moderate—moderate, and 7) moderate—exceptionally high. Only the low—moderate profile was found across all three measurement points. MSC increased over time, while normative stances remained stable or declined. Children who transitioned from high—moderate (T1) to moderate—low (T2) to moderate—moderate (T3) shared more at T3 than those who remained at low—moderate levels. Gender and socioeconomic differences were found, highlighting the role of individual and family characteristics on moral development. Overall, no consistent pattern was found between profiles and sharing behavior, emphasizing the complex nature of moral development and prosocial behavior in early childhood.
Schiele, T., Valcárcel Jiménez, M., Camligüney, Ö., & Niklas, F. (accepted, July 2026). From Norms to Action: Exploring Latent Profile Transitions of Moral Self-Concept and Normative Stances in Young Children’s Sharing Behavior. Child Development.
PDF: Latent Profiles (normative stances—moral self-concept) across all three measurement points (PDF, 103 KB)
Profiles of the Home Literacy Environment and Children’s Literacy Skills
Profile of the Home Literacy Environment
Note. fHLE = formal home literacy environment. Profile 1 = “very low HLE” (n = 20, 4.1%); Profile 2 = “average HLE” (n = 230, 47.4%); Profile 3 = “few books–above average formal HLE” (n = 84, 17.3%); Profile 4 = “high HLE–average formal HLE” (n = 96, 19.8%); Profile 5 = “average HLE–very frequent parental reading” (n = 55, 11.3%). The five indicators are represented from left to right as follows: reading aloud (first/leftmost bar), child interest (second bar), access to books (third bar), fHLE (fourth bar), and parental reading (fifth/rightmost bar). Positive values show an above-average frequency of the indicator. Negative values show a below-average frequency of the indicator.
July 20, 2026
The Home Literacy Environment (HLE) plays a crucial role in fostering children’s literacy skills. However, longitudinal person-centered investigations examining how combinations of HLE indicators predict literacy development across heterogeneous samples and educational contexts, particularly in Germany, remain limited. In a sample of N = 500 preschool children (Mage = 60.97; SDage = 4.61; 51% girls; 38% migration background) in Germany, we found five profiles differing across five HLE indicators (reading aloud, child interest in reading, access to books, formal HLE, and parental reading behavior): (1) very low HLE, (2) average HLE, (3) few books–above-average formal HLE, (4) high HLE–average formal HLE, and (5) average HLE–very frequent parental reading. Child and family characteristics were significant predictors of HLE profile membership. For example, the likelihood of being in profile 1 (very low HLE) was higher for boys, children with a migration background, with lower socioeconomic status, and whose parents had fewer positive attitudes towards reading. Profile membership was partly associated with vocabulary, phonological awareness, and letter knowledge skills across three measurement points from preschool to the beginning of Grade 1. For example, compared to children in the other profiles at T1, those in the very low HLE profile had the lowest vocabulary, whereas no significant associations between profiles and phonological awareness or letter knowledge were found. A comparison with a variable-centered model showed the additional benefits of examining specific patterns across HLE indicators, especially for answering the question for whom and under what conditions an educational intervention or an instructional program at home could be effective.
Valcárcel Jiménez, M., Edelsbrunner, P., Schiele, T., Camligüney, Ö., & Niklas, F. (accepted, June 2026). Unveiling Patterns: Understanding the Relations Between Profiles of the Home Literacy Environment, Family Background Characteristics, and Preschool Children’s Literacy Development. Journal of Educational Psychology.
Launch of the New SHARP Website
On June 11, 2026, the new SHARP website went live. It offers insight into the Transregio Collaborative Research Centre on personalized learning and teaching through simulations in higher education and presents the consortium’s goals, project areas, and participating scientific institutions. The website will serve as a central point of reference for information and events about SHARP. Direct Link
New Open-Access BJEP Article on Learning With Worked Examples
01.06.2026
We are pleased to announce the publication of our new article in the British Journal of Educational Psychology. The paper is available Open Access.
The article develops a three-component model of learning that integrates instructional support, engagement activities, and learner aptitudes. We argue that all three components influence learning outcomes, interact with one another, and should therefore be considered jointly.
Empirically, the model is examined in the context of worked examples. In this framework, self-explanation quality is conceptualized as the engagement activity through which instructional support may influence learning. The findings indicate an indirect effect of worked examples via self-explanation quality – albeit only for certain learners and thus depending on cognitive learner characteristics.
We discuss implications for instructional design, theories of learning, and research on individual differences.
In addition, the study constitutes a replication study that aims to contribute to a more cumulative evidence base in educational psychology. Data and study materials are publicly available on OSF.
SHARP Laboratory Network Established
18.02.2026
The SHARP Laboratory Network has been established. It connects existing labs and infrastructures across LMU Munich, TUM, and LMU University Hospital (LMU Klinikum) to jointly advance research, teaching, and transfer in simulation-based educational settings. The network bundles expertise and resources, supports coordinated access to equipment and environments, and fosters exchange on methods, data, and shared standards — for example in eye-tracking, VR/XR, video and classroom recordings, and other digital learning and training scenarios.
The DFG Collaborative Research Center SHARP (TRR419) has been approved
July 1st, 2025
The Chair of Empirical Education and Educational Psychology is delighted that the German Research Foundation (DFG) will be funding the new Collaborative Research Center SHARP (TRR 419) from October 1, 2025. Several members of the chair are involved as project leaders.
Personalized, simulation-based learning can better prepare students for the complex demands of practical work. "We are researching the conditions and mechanisms of acquiring complex skills with AI-supported simulations," explains Prof. Dr. Frank Fischer (LMU), spokesperson for the SFB. "The knowledge we are developing in SHARP has the potential to drive innovation in higher education teaching and evidence-based practice," emphasizes deputy spokesperson Prof. Dr. Tina Seidel (TUM).
SHARP is the first collaborative research center in educational research and aims to develop important foundations for innovation in higher education. The SFB brings together the disciplines of medicine, biology, chemistry, computer science, mathematics, physics, education, and psychology. In addition to LMU and TUM, the University of Augsburg and the Leibniz Supercomputing Centre are also involved. More information can be found here.
Lecture Series on Methodological Innovations in Learning Sciences
November 26, 2024
As part of the ChAN cooperation project 2024 with Peking University, we presented an online Distinguished China-Germany Lecture Series on learning sciences entitled "Methodological Advancements" on October 23, 2024 . Four renowned scientists from China and Germany in the field of learning sciences—Dr. Lu Zhang, Prof. Dr. Matthias Stadler, Dr. Yu Liu, and Dr. Olga Chernikova— gave lectures on topics such as educational games, generative AI in medical and teacher training, and AI-supported systematic literature analyses.
The lecture series was commented on and summarized by the ChAN project leaders: Prof. Frank Fischer, Director of the Munich Center of the Learning Sciences, and Prof. Junjie Shang, Executive Director of the Lab of Learning Sciences at Peking University. Nearly 100 participants attended this lecture series.
Learning4Kids: Enhancing Educational Opportunities through Learning Apps
LMU Munich psychologist Frank Niklas has investigated how children can strengthen their mathematical and literacy skills through play before starting school (video in German on Youtube)
2:20 | 13 Jan 2025