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.
Highlights
Statistics is a challenging subject for many university students
Our first question from the introduction – whether the fields of didactics of statistics and inspectable student models can be combined – can be explicated as follows: (RQ1) Are inspectable student models suitable for implementation in didactically grounded, sequential statistics modules consisting of closely related tasks? The second question, whether the two fields can strengthen each other, focuses on the evaluation methods available in both fields: (RQ2) How can didactical analysis inform design of inspectable student models and, vice versa, how can student model evaluation methods inform didactical design?
We present the main quality assessment of the implemented domain models: learning curve analysis. The results of this form the starting point for didactical task analysis. This leads to the identification of four problems in implementing inspectable student models in rich instructional modules for statistics and to possible improvements of the domain models, Q-matrices and the instructional modules to resolve these four problems
Summary
Statistics is a challenging subject for many university students. In addition to dedicated methods of didactics of statistics, adaptive educational technologies can offer a promising approach to target this challenge. Accomplishing this shift involves specific didactical considerations in instructional design, such as using real contexts and data for promoting meaningful statistical reasoning (Ben-Zvi 2000) Another possible enhancement of statistics education, which is especially relevant when individual guidance by teachers is difficult to achieve, comes from a different area: adaptive educational technologies (Herder et al 2017). Adaptive educational systems elicit this information based on students’ interaction with learning content: solving tasks, taking tests, studying examples, etc Presenting this information to students as feedback and allowing them to inspect it freely is known to promote reflection, increase motivation and provide metacognitive support for self-regulated learning (Bull and Kay 2007). An inspectable student model can support a student in forming an opinion about his or her current progress and making a well-considered decision about the learning step (which concepts to focus on, which task to attempt, etc.)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
