The Interplay between Inspectable Student Models and Didactics of Statistics

Abstract

Statistics is a challenging subject for many university students. In addition to dedicated methods of didactics of statistics, adaptive educational technologies can also offer a promising approach to target this challenge. Inspectable student models provide students with information about their mastery of the domain, thus triggering reflection and supporting the planning of subsequent study steps. In this article, we investigate the question of whether insights from didactics of statistics can be combined with inspectable student models and examine if the two can reinforce each other. Five inspectable student models were implemented within five didactically grounded online statistics modules, which were offered to 160 Social Sciences students as part of their first-year university statistics course. The student models were evaluated using several methods. Learning curve analysis and predictive validity analysis examined the quality of the student models from the technical point of view, while a questionnaire and a task analysis provided a didactical perspective. The results suggest that students appreciated the overall design, but the learning curve analysis revealed several weaknesses in the implemented domain structure. The task analysis revealed four underlying problems that help to explain these weaknesses. Addressing these problems improved both the predictive validity of the adjusted student models and the quality of the instructional modules themselves. These results provide insight into how inspectable student models and didactics of statistics can augment each other in the design of rich instructional modules for statistics.

Combined inner and outer loop feedback in an intelligent tutoring system for statistics in higher education

“…The student model design was evaluated in two earlier courses (Tacoma, Sosnovsky, Boon, Jeuring, & Drijvers, 2018). Participants in the current study had been enrolled in one of these earlier courses and, consequently, were already familiar with inspectable student models.…”

Enhancing learning with inspectable student models: Worth the effort?

“…Earlier studies revealed that students value the presence of inspectable student models in weekly homework sets (Mitrovic & Martin, 2007;Tacoma, Sosnovsky, Boon, Jeuring, & Drijvers, 2018). To our knowledge, previous research focused on the effects of inspectable student models combined with either (1) task selection adapted by the electronic learning environment based on the content of the student model (e.g., Brusilovsky, Sosnovsky, & Yudelson, 2009), or (2) monitoring of and feedback on task selection by the student (Mitrovic & Martin, 2007).…”

“…Most tasks in the homework sets were connected to the topic(s) they were related to. Lists of topics, connections between tasks and topics and the tasks themselves were optimized informed by findings by Tacoma et al (2018), based on the same course in the previous academic year. In particular, this previous study showed that some tasks served a useful function in the homework set (such as introducing a new topic), but were not appropriate for informing student models, and hence should Fig.…”

Medidas de tendencia central y dispersión miradas desde un deporte típico chileno y la modelación estadística

“…Al respecto, Cobb (2007) sugiere que la modelación debería desempeñar un papel importante en la enseñanza de la estadística, afirmando que todos los métodos estadísticos se derivan de un modelo y que uno de los propósitos de la educación matemática es fomentar el pensamiento estadístico en los estudiantes a través de un razonamiento explícito entre el modelo y la realidad. El desarrollo de la modelación estadística ayuda a enfrentar las dificultades de aprendizaje de la estadística, permitiendo al estudiante desarrollar la capacidad de resolución de problemas (Tacoma et al, 2018).…”