The learning sciences investigate learning and teaching, do related theory-building and method development, and work to transfer their insights into practice (Fischer, 2018). The present essay provides an overview of these activities, first covering the learning sciences’ defining characteristics and how academic programs train learning scientists. Next, design-based research (DBR)’s and learning analytics’ merits and shortcomings are discussed, as they are among learning sciences’ signature methods (Fischer, 2018). In closing, applications of learning sciences’ scaffolding and multimedia principles are reviewed. Concluding remarks are provided in the final section.
The learning sciences first emerged about 30 years ago (Kolodner, 2004). The field has since grown, with researchers from psychology, educational sciences, computer science, philosophy, and others joining forces to inquire and enhance learning and teaching. Hence, one defining characteristic is the learning sciences’ interdisciplinarity, rooted in the conviction that this will lead to a more holistic understanding of learning (Fischer et al., 2018).
The learning sciences are viewed as a community of practice due to their collaboration, mutual engagement, common goal, and shared set of concepts and methods (Sommerhoff et al., 2018). Notably, the learning sciences do application-oriented research, i.e., empirical work of practical use and embedded in real-world contexts (Fischer et al., 2018). This differentiates them from similar fields such as cognitive science, which tend to deprioritize socio-contextual factors (Hoadley, 2018). Learning scientists also use various qualitative and quantitative tools in their inquiry, with different approaches and use cases for each, which fits the broad research matter of learning and teaching. Empiricism lies at the core of these methods (Hoadley, 2018).
Whether the learning sciences are its own discipline remains debated (Hoadley, 2018). Arguably, an acknowledged research discipline would be granted a university department (Chandler, 2009). However, to my knowledge, no learning sciences departments currently exist within higher education. Learning sciences’ only formal space is related scientific journals (e.g., the Journal of the Learning Sciences) and academic conferences (e.g., the ISLS Annual Meeting). However, there are now several learning sciences master’s and Ph.D. programs.
Learning Sciences Programs
How future learning scientists are trained at academic institutions paints a picture of current methodological and conceptual foci. Sommerhoff et al. (2018) report that most graduate programs in the learning sciences prioritize DBR and “traditional statistics” in their methods curriculum. They also show a conceptual focus on technology-supported learning, cognition, learning in informal contexts (e.g., museums), and learning design. LMU Munich’s program favors frequentist statistics, traditional psychological research methods, and psychometrics (i.e., 24 out of 120 ECTS) but includes no courses on DBR. This is an interesting deviation, as Sommerhoff et al. (2018) report DBR is covered in 1 out of 2 psychology-affiliated learning sciences programs. At the same time, the LMU program’s conceptual content is mainly aligned with the study’s other programs, as it centers around technology-supported learning (P1.2 and WP4), cognition (P1.1 and WP3), and domain-specific learning (WP1-6). Interestingly, there are no learning design courses but others on organizational learning and meta-level topics (e.g., educational systems), which few to none of the programs in the study report. Notably, both the master’s thesis project and internship allow LMU students to pick their topics. These two modules make up one-third of the program’s ECTS, leaving much of the methodological and conceptual focus up to students’ choice.