LAK of Direction: Misalignment Between the Goals of Learning Analytics and its Research Scholarship - Motz, Benjamin A.; Bergner, Yoav; Brooks, Christopher A.; Gladden, Anna; Gray, Geraldine; Lang, Charles; Li, Warren; Marmolejo-Ramos, Fernando; Quick, Joshua D. (Journal of Learning Analytics, 2023)
Learning analytics defines itself with a focus on data from learners and learning environments, with corresponding goals of understanding and optimizing student learning.  In this regard, learning analytics research, ideally, should be characterized by studies that make use of data from learners engaged in education systems, should measure student learning, and should make efforts to intervene and improve these learning environments. However, a common concern among members of the learning analytics research community is that these standards are not being met.  In two analysis waves, we review a large and comprehensive sample of research articles from the proceedings of the three most recent Learning Analytics and Knowledge conferences, the premier conference venue for learning analytics research, and from articles published during the same time in the Journal of Learning Analytics (over the years of 2020, 2021, and 2022).  We find that 37.4% of articles do not analyze data from learners in an education system, 71.1% do not include any measure of learning, and 89.0% of articles do not attempt to intervene in the learning environment.  We contrast these findings with the stated definition of learning analytics and infer, like others before us, that scholarship in learning analytics research presently lacks clear direction toward its stated goals.  We invite critical discussion of these findings from the learning analytics community, through open peer commentary.