Search for: All records

The “Doer Effect” is the empirical phenomenon observed as a stronger correlational relationship between students who complete more activities and their course learning outcomes compared to those who complete fewer activities or watch fewer videos. In this paper, we extended prior evidence of a “Doer Effect” to investigate how doing more can be related not only to better learning outcomes but also to motivational ones. Specifically, we investigated persistence as the student’s willingness to continue working on course activities. We used secondary analyses of data from MOOC that taught Advanced Placement (AP) Introductory Java Programming to high school students using the digital textbook platform RuneStone. Although we failed to identify a doer effect in learning outcomes, our analyses do suggest that completing more activities is related to longer persistence in the course than reading more pages or watching more videos. This effect does not appear to be limited to highly motivated students. 

There is a growing community of researchers at the intersection- tion of data mining, AI, and computing education research. The objective of the CSEDM workshop is to facilitate a dis- Discussion among this research community, with a focus on how data mining can be uniquely applied in computing ed- ucation research. For example, what new techniques are needed to analyze program code and CS log data? How do results from CS education inform our analysis of this data? The workshop is meant to be an interdisciplinary event at the intersection of EDM and Computing Education Research. Researchers, faculty, and students are encouraged to share their AI- and data-driven approaches, methodological- gies, and experiences where data transforms how students learn Computer Science (CS) skills. This full-day workshop will feature paper presentations and discussions to promote collaboration. 

Many college students drop STEM majors after struggling in gateway courses, in part because these courses place large demands on students9 time. In three online experiments with two different lessons (measures of central tendency and multiple regression), we identified a promising approach to increase the efficiency of STEM instruction. When we removed lectures and taught participants exclusively with practice and feedback, they learned at least 15% faster. However, our research also showed that this instructional strategy has the potential to undermine interest in course content for less-confident students, who may be discouraged when challenged to solve problems without upfront instruction and learn from their mistakes. If researchers and educators can develop engaging and efficacy-building activities that replace lectures, STEM courses could become better learning environments. 

What does it mean to be a better model? One conceptualization, indeed a common one in Educational Data Mining, is that a better model is the one that fits the data better, that is, higher prediction accuracy. However, oftentimes, models that maximize prediction accuracy do not provide meaningful parameter estimates. Here we argue that models that provide meaningful parameters are better models and, indeed, often also provide higher prediction accuracy. To illustrate our argument, we investigate the Performance Factors Analysis (PFA) model and the Additive Factors Model (AFM). PFA often has higher prediction accuracy than the AFM; however, PFA申fs parameter estimates are ambiguous and confounded. We propose more interpretable models (AFMh and PFAh) designed to address the confounded parameters and demonstrate PFA申fs confounding issues with synthetic data. The results from the experiment with 27 real-world dataset also support our claims and show that the more interpretable models can produce better predictions. 

Cognitive science of learning points to solutions for making use of existing study and instruction time more effectively and efficiently. However, solutions are not and cannot be one-size-fits-all. This paper outlines the danger of overreliance on specific strategies as one-size-fits-all recommendations and highlights instead the cognitive learning processes that facilitate meaningful and long-lasting learning. Three of the most commonly recommended strategies from cognitive science provide a starting point; understanding the underlying processes allows us to tailor these recommendations to implement at the right time, in the right way, for the right content, and for the right students. Recommendations regard teacher training, the funding and incentivizing of educational interventions, guidelines for the development of educational technologies, and policies that focus on using existing instructional time more wisely.