Marc Philipp Janson

On September 1, 2024, Jun. Prof. Dr. Marc Philipp Janson took up the tenure-track professorship in Educational Psychology with a focus on Educational Design and Educational Effectiveness at the University of Education Karlsruhe. At the Institute of Psychology, his research focuses on self-regulated learning (SRL) in intelligent tutoring systems (ITS). Within his research group, Julia Hilpert, Dana Deeva, and Theresa Dechamps are pursuing their doctoral dissertations.

Previously, Marc Philipp Janson worked at the University of Mannheim, where he continues to lead a research project until 2026. At PHKA, he is part of the management team of the research and early-career college "Task Quality in Digitally Supported Instruction (AQUA-d)," where he is also responsible for the subproject "Variability in Digital Retrieval Practice." Before pursuing his academic career, he was involved in developing the intelligent tutoring system CoTutor, which he continues to use for research purposes.

He can be reached at janson(at)ph-karlsruhe.de.

Research Areas and Interests

Self-Regulated Learning in Digital Learning Environments

Initiating and maintaining goal-directed learning activities is a major self-regulatory challenge (Schunk & Zimmerman, 2023; Zimmerman & Schunk, 2011). This also applies to digital learning environments (Azevedo et al., 2011; Winters et al., 2008). In his research, Marc Philipp Janson investigates interindividual and intraindividual differences in self-regulated learning behavior, their antecedents, and their predictive validity. The use of behavioral data from ecologically valid digital learning environments provides unique insight into actual learning behavior and complements data gathered through self-report measures alone. He is currently conducting several research programs together with various collaboration partners, each pursuing different theoretical approaches.

• Motivation and Procrastination:
  Drawing on situated expectancy-value theory (Eccles & Wigfield, 2024) and temporal motivation theory (Steel, 2007), Marc Philipp Janson investigates the extent to which motivational components predict learning behavior, particularly with respect to temporal landmarks (e.g., upcoming exams). This research has already made it possible to test temporal motivation theory using real behavioral data (Janson, Wenker et al., 2024).
  Collaboration partner: Dr. Lisa Bäulke (Hector Institute Tübingen)
• Affect and Emotion:
  Affective and emotional experience is subject to considerable intraindividual fluctuation during self-regulated learning. Building on the control-value theory of achievement emotions (Pekrun et al., 2023), Marc Philipp Janson investigates which specific learning situations are decisive for the emergence of affect and emotion. Initial published findings show, in particular, that the high intraindividual variability in experience poses a particular challenge for the granularity of measurement and the temporal proximity to the predicted learning behavior (Hilpert & Janson, 2026).
  Doctoral research project: Julia Hilpert (PHKA)
• Metacognition:
  Learners' own perception of their learning progress is a key process in self-regulated learning (Soderstrom et al., 2016). A central object of study here is so-called judgments of learning (JOL, Koriat, 1997) — learners' self-assessed confidence that they have solved a task correctly — which is well established in experimental research. His research investigates the extent to which the predictive validity of such metacognitive judgments also generalizes to ecologically valid field conditions. This is particularly important, as existing experimental findings based on artificial learning materials only partially generalize to learning with relevant, meaningful content (e.g., Ingendahl & Undorf, 2024). Initial findings already show that JOLs predict learning performance and the allocation of study effort (Janson, Wissel et al., 2026).
  Collaboration partners: Prof. Dr. Monika Undorf, Dr. Franziska Ingendahl (TU Darmstadt); Prof. Dr. Stefan Münzer, M.Sc. Samuel Wissel (University of Mannheim)

Evaluation and Optimization of Intelligent Tutoring Systems (ITS)

A further focus of his research is the investigation, development, and evaluation of digital learning systems, particularly intelligent tutoring systems (ITS; Kulik & Fletcher, 2016; Mousavinasab et al., 2021), that support learners in digital self-regulated learning (Azevedo et al., 2011; Schunk & Zimmerman, 2023; Winters et al., 2008; Zimmerman & Schunk, 2011). Within this area, Marc Philipp Janson pursues several research projects:

• Fitting Feedback (doctoral research project):
  Feedback effects generally vary considerably (Hattie & Timperley, 2007; Kluger & DeNisi, 1996; Wisniewski et al., 2020), including in the context of practice testing (Adesope et al., 2017; Naujoks et al., 2022). Janson's research has focused on increasing the effectiveness of informative feedback by adapting it to interindividual differences. For this purpose, he integrated several theoretical approaches (Higgins, 2000; Kluger & DeNisi, 1996) into the theory of Fitting Feedback, which proposes that framing performance feedback in line with learners' own strategic orientations can foster motivation and performance. The research program conducted to date comprises six empirical studies examining differently framed performance feedback in the context of practice testing (Janson, Siebert et al., 2022; 2023; Janson & Dickhäuser, 2025).
  Supervisor: Prof. Dr. Oliver Dickhäuser (University of Mannheim)
• Predictive Validity of Learning Behavior in ITS for Academic Success:
  The learning data generated through the lecture-accompanying use of intelligent tutoring systems are also examined with respect to learners' academic success. Specifically, Janson investigates whether performance indicators from intelligent tutoring systems explain exam success incrementally, beyond existing performance indicators (e.g., high school GPA). He also examines the relationship between different learning strategies and academic success, such as the negative association between massed learning and exam performance. Initial book chapters highlight findings from the lecture-accompanying use of the ITS CoTutor (Münzer et al., 2026; Parrisius et al., 2026).
  Collaboration partners: Prof. Dr. Stefan Münzer, Dr. Benedict C. O. F. Fehringer, M.Sc. Samuel Wissel (University of Mannheim)
• Variability in Retrieval Practice:
  Marc Philipp Janson's research examines the extent to which semi-generative learning content, or greater variability, can enhance learning outcomes in the sense of so-called desirable difficulties (Bjork & Bjork, 1992; 2011). Building on the assumption that the generally high effectiveness of retrieval practice (Adesope et al., 2017) is limited by learners recognizing surface features upon repetition, he investigates whether different task variations positively influence learning behavior and learning outcomes.
  Doctoral research project: Dana Deeva (AQUA-d subproject 5)
  Collaboration partners: M.Sc. Paula Schmelzer (RISC project, University of Mannheim), Prof. Dr. Andreas Lachner (University of Tübingen), Prof. Shana K. Carpenter, PhD (Oregon State University)

Meta-Analysis on Reference Norm Orientation

Achievement evaluation refers to assessing results against particular comparison standards (Heckhausen, 1974). Different standards, or reference norms, can be used to evaluate performance. Under a criterion-referenced norm, a result is evaluated against fixed, objective criteria. Beyond this, evaluators may also draw on intraindividual and interindividual comparisons. Under an individual reference norm, current performance is compared with a person's own previous performance. Under a social reference norm, performance is instead evaluated against the performance of others. Research on teachers' reference norm orientations has been highly productive (Rheinberg, 1980; 1982; see also Mischo & Rheinberg, 1995; Rheinberg & Krug, 1993). With few exceptions (Dickhäuser et al., 2017; Lüdtke et al., 2005; Retelsdorf & Günther, 2011), however, the existing literature remains largely confined to German-speaking countries.

An up-to-date systematic review of the existing research, along with a quantitative synthesis of prior findings, is still lacking. Marc Philipp Janson addresses this research gap with a meta-analysis on reference norm orientation. Initial analyses have already been presented at academic conferences.

Supervised Doctoral Dissertations

• Julia Hilpert: Fostering Self-regulated Learning in Intelligent Tutoring Systems (Start: September 1, 2024)
• Dana Deeva: Variability as desirable difficulty to leverage technology-enhanced retrieval practice (Start: September 1, 2026)
• Theresa Dechamps: Cognitive load theory and digital learning media: an evaluation of a cognitive cost-benefit model (Supervision taken over: August 1, 2026)

Externally Funded Research Projects

• Research and Early-Career College "Task Quality in Digitally Supported Instruction (AQUA-d)," Funding Phase II, Subproject 5 "Variability in Digital Retrieval Practice" (Ministry of Science, Research and the Arts Baden-Württemberg; subproject lead and part of overall leadership; total funding: €2,400,000.00)
• Research project "Understanding and Supporting Self-regulated Learning in Intelligent Tutoring Systems" (RISC funding, University of Mannheim: €79,900.00)
• Doctoral research project "Fitting Feedback" (Doctoral funding, GESS University of Mannheim: €25,800.00; Doctoral funding, Konrad Adenauer Foundation: €52,200.00)

Selected Publications

Journal Articles (peer-reviewed)

• Hilpert, J., & Janson, M. P. (2026). Tracing affect in digital learning: Combining log data and introspective momentary assessments, Computers & Education, 105668.
• Janson, M. P., Wissel, S., Ingendahl, F., & Undorf, M. (2026). Judgments of learning in the wild: Establishing ecological validity with an intelligent tutoring system in a field study. Learning and Instruction, 103, 102293.
• Janson, M. P., & Dickhäuser, O. (2025). More fit, less load? Cognitive processes of feedback framed toward own preferences. The Journal of Experimental Education, 93(2), 261–281.
• Janson, M. P., Wenker, T., & Bäulke, L. (2024). Only a matter of time? Using logfile data to evaluate temporal motivation theory in university students' examination preparation. British Journal of Educational Psychology, 1–16.
• Janson, M. P., Siebert, J., & Dickhäuser, O. (2023). Everything right or nothing wrong? Regulatory fit effects in an e-learning context. Social Psychology of Education, 26, 107–139.
• Janson, M. P., Siebert, J., & Dickhäuser, O. (2022). Compared to what? Effects of social and temporal comparison standards of feedback in an e-learning context. International Journal of Educational Technology in Higher Education, 19, 1–26.
• Janson, M. P., & Dickhäuser, O. (2019). Prüfungsvorbereitung an der Hochschule mittels Lernsoftware: Die Effekte von Wenn-Dann-Plänen. Unterrichtswissenschaft, 47(3), 295–312.

Book Chapters

• Münzer, S., Wissel, S., Şatır, H. D., Fehringer, B. C. O. F., & Janson, M. P. (2026). Vorlesungsbegleitendes digitales Tutoring mit CoTutor zur Unterstützung von kognitiven Lernstrategien bei Erstsemesterstudierenden. In M. Friedrich-Schieback, M. Klinger, & N. Baldus (Eds.), Freiräume für innovative digitale Lehre: Gestaltungsprozesse, Lehrprojekte, Reflexionen (pp. 89–114). wbv.
• Parrisius, C., Holz, H., Bez, S., Merk, S., & Janson, M. P. (2026). Intelligente tutorielle Systeme. In U. Trautwein, M. Hasselhorn, C. Mähler, & T. Richter (Eds.), Trends und Innovationen pädagogisch-psychologischer Diagnostik (pp. 123–140). Hogrefe.