The Art of Modelling the Learning Process

By uniting key concepts and methods from education, psychology, statistics, econometrics, medicine, language, and forensic science, this textbook provides an interdisciplinary methodological approach to study human learning processes longitudinally. This longitudinal approach can help to acquire a better understanding of learning processes, can inform both future learning and the revision of educational content and formats, and may help to foster self-regulated learning skills. The initial section of this textbook focuses on different types of research questions as well as practice-driven questions that may refer to groups or to individual learners. This is followed by a discussion of different types of outcome variables in educational research and practice, such as pass/fail and other dichotomies, multi-category nominal choices, ordered performance categories, and different types of quantifiable (i.e., interval or ratio level of measurement) variables. For each of these typesof outcome variables, single-measurement and repeated-measurements scenarios are offered with clear examples. The book then introduces cross-sectional and longitudinal interdependence of learning-related variables through emerging network-analytic methods and in the final part the learned concepts are applied to different types of studies involving time series. The book concludes with some general guidelines to give direction to future (united) educational research and practice. This textbook is a must-have for all applied researchers, teachers and practitioners interested in (the teaching of) human learning, instructional design, assessment, life-long learning or applications of concepts and methods commonly encountered in fields such as econometrics, psychology, and sociology to educational research and practice.

Links educational research and practice through examples of theory-driven, data-driven, and practice-oriented research Applies concepts and methods from a range of social and health science areas into an interdisciplinary methodological approach Provides methodological and statistical analysis plans for a wide range of research questions and designs Unites research approaches that tend to have different units of analysis Provides examples from a wide variety of statistical software

Auteur
Jimmie Leppink (28 April 1983) obtained degrees in Psychology (MSc, September 2005 - July 2006, Cum Laude), Law (LLM, September 2007 - July 2008), and Statistics Education (PhD, September 2008 - March 2012) from Maastricht University, the Netherlands, and obtained a degree in Statistics (MSc, October 2011 - July 2012, Magna Cum Laude) from the Catholic University of Leuven, Belgium. He defended his PhD Thesis in Statistics Education in June of 2012, and was a Postdoc in Education (April 2012 - March 2017) and Assistant Professor of Methodology and Statistics (April 2017 - January 2019) at Maastricht University's School of Health Professions Education. Since January 2019, he has been working as a Senior Lecturer in Medical Education at Hull York Medical School, which is a joint medical school of the University of Hull and the University of York. His research, teaching, and consulting activities revolve around applications of quantitative methods in Education, Psychology, and a broader Social Science context as well as the use of learning analytics for the design of learning environments, instruction, and assessment in Medical Education and the broader Higher Education.

Contenu
Part I Common Questions.- Chapter 1: Learning Processes.- Chapter 2: Study Designs.- Chapter 3: Statistical Learning.- Chapter 4: Anchoring Narratives.- Part II Variable Types.- Chapter 5 Pass/Fail and Other Dichotomies.- Chapter 6 Multicategory Nominal Choices.- Chapter 7 Ordered Performance Categories.- Chapter 8 Quantifiable Learning Outcomes.- Part III Variable Networks.- Chapter 9 Instrument Structures.- Chapter 10 Cross-Instrument Communication.- Chapter 11 Temporal Structures.- Chapter 12 Longitudinal Assessment Networks.- Part IV Time Series.- Chapter 13 Randomised Controlled Experiments.- Chapter 14 Static and Dynamic Group Structures.- Chapter 15 Progress Testing in Large Cohorts.- Chapter 16 Small Samples and Case Studies.- Part V Conclusion.- Chapter 17: General Recommendations.