This research explores the potential of the Computerized Adaptive Learning (CAL) technology to support an active learning class in a statistical inference course. The distinguishing feature of these systems is that each student receives a different set of items: the student's skill/knowledge is measured from the responses of the items. Then, a new item is given to each student based on their skill/knowledge previously measured. CAL system proposed here includes a bank of 50 items of the topic: ‘hypothesis testing’.

The platform employed to apply the CAL system is at the second prototype stage. It is still under construction now. The first prototype consisted in using the open-source Concerto platform to apply the tool. The tool was applied on two sections of an Econometric course belonging to the Industrial Engineer carrier at the Universidad Diego Portales (26 and 21 students) the second semester 2022. The scheme of the application considered: 5 minutes explanation, 20 minutes using the test, 20 minutes of teacher’s feedback, 20 minutes using the test, and 10 minutes answering a satisfaction survey. In the first section, the feedback included a formal explanation of the topic, while in the second section, the teacher only answered students’ questions. The second prototype is an app build in R that employs the Glicko ranking system to rank both, student’s abilities, and item’s difficulties.

Preliminary results on the first prototype suggest that the activity was enjoyed by 50% of the students and that satisfaction was similar in both sections. Besides, clear differences were observed between sections: in the section with expositive feedback the students reduced answering times and the tool moved to more difficult stems. On the other side, the students that received ‘answering questions’ as feedback did not improve their results after the this. The second prototype will be proved in three sections of the course “Statistical Inference” (last week of November 2022).

Bio: Paula Fariña is an Economist at the Universidad de San Andrés in Buenos Aires, Argentina (2001), and PhD. in Statistics at the Universidad Católica de Chile (2010). In 2011, she did a postdoctoral research at the Centro de Estudios de Políticas y Práticas en Educación (CEPPE). Since 2012 she has been an assistant professor at the Industrial Egineering School, Facultad de Ingeniería y Ciencias, Universidad Diego Portales. Her research work has been focused on psychometrics, educational statistics, discrete choice models, statistics applied to social sciences. Her present areas of interest are also Bayesian statistics  and data science.

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