Evaluation of Parsons Problems with Menu-Based Self-Explanation Prompts in a Mobile Python Tutor
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Evaluation of Parsons Problems with Menu-Based Self-Explanation Prompts in a Mobile Python Tutor Geela Venise Firmalo Fabic 1 & Antonija Mitrovic 1
& Kourosh
Neshatian 1
# International Artificial Intelligence in Education Society 2019
Abstract The overarching goal of our project is to design effective learning activities for PyKinetic, a smartphone Python tutor. In this paper, we present a study using a variant of Parsons problems we designed for PyKinetic. Parsons problems contain randomized code which needs to be re-ordered to produce the desired effect. In our variant of Parsons problems, students were asked to complete the missing part(s) of some lines of code (LOCs), and rearrange the LOCs to match the problem description. In addition, we added menu-based Self-Explanation (SE) prompts. Students were asked to self-explain concepts related to incomplete LOCs they solved. Our hypotheses were: (H1) PyKinetic would be successful in supporting learning; (H2) menu-based SE prompts would result in further learning benefits; (H3) students with low prior knowledge (LP) would learn more from our Parsons problems in comparison to those with high prior knowledge (HP). We found that the participants’ scores on the post-test improved, thus showing evidence of learning in PyKinetic. The experimental group participants, who had SE prompts, showed improved learning in comparison to the control group. Further analyses revealed that LP students improved more than HP students and the improvement is even more pronounced for LP learners who self-explained. The contributions of our work are a) pedagogically-guided design of Parsons problems with SE prompts used on smartphones, b) showing that our Parsons problems are effective in supporting learning and c) our Parsons problems with SE prompts are especially effective for students with low prior knowledge. Keywords Mobile Python tutor . Menu-based self-explanation . Parsons problems
* Antonija Mitrovic [email protected]
1
Department of Computer Science and Software Engineering, University of Canterbury, Christchurch, New Zealand
International Journal of Artificial Intelligence in Education
Introduction Understanding programming concepts and acquiring the skill of code writing are both essential in learning programming. Novices struggle with problem solving due to the lack of declarative and/or procedural knowledge (Anderson 1982). In programming, declarative knowledge includes the syntax of the programming language, while procedural knowledge comprises of writing code constructs into a meaningful working program. The goal of our project is to support novices in learning Python by having simple activities that support acquisition of both conceptual and procedural knowledge. We present PyKinetic, a Python tutor for Android smartphones which is designed for novices and aimed as a complement to traditional lectures (Fabic et al. 2016a, b). Mobile learning is proven effective in many domains including computing education (Hürst et al. 2007; Karavirta et al. 2012; B
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