Efficiently Prompting Students When Developing Computational Thinking Skills: The Impact of Students' Response Modality

Efficiently Prompting Students When Developing Computational Thinking Skills: The Impact of Students' Response Modality

Soumela K. Atmatzidou (Aristotle University of Thessaloniki, Greece), Chrysanthi N. Βekiari (Aristotle University of Thessaloniki, Greece), and Stavros N. Demetriadis (Aristotle University of Thessaloniki, Greece)
Copyright: © 2022 |Pages: 25
DOI: 10.4018/978-1-7998-7443-0.ch005
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This study investigates the impact of student response modality on the development of computational thinking skills in educational robotics activities. Students of an elementary school were divided into three study groups ('Control', 'Selecting', and 'Writing') that implemented activities based on the same teacher guidance while prompted to provide responses of different modalities. The purpose was to engage students in the development of computational thinking skills, focusing on the basic skills of abstraction, generalization, algorithm, modularity, and debugging. These skills were evaluated at different phases during the activity, using different modality (selection, written, and oral) assessment tools. The results suggest that (1) prompting and eliciting thoughts in the form of written or selected answers proves to be a beneficial strategy, and (2) the two groups, ‘Writing' and ‘Selecting', reach the same level of CT skills, which is significantly higher than the level of the control group.
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Educational Robotics (ER) is a constructivist teaching and learning tool which promotes the development of students' computational thinking (CT) and social skills. Through ER activities, students are motivated and guided to solve authentic problems by programming the behaviour of a physical object (the robot) and getting immediate feedback on their proposed solutions. Researchers argue that ER is a problem-based learning tool which facilitates collaboration, enhances conceptual understanding and critical thinking, and promotes higher-order learning in all scientific fields (e.g. Atmatzidou & Demetriadis, 2016; Atmatzidou, Demetriadis, & Nika, 2018; Eteokleous-Grigoriou & Psomas, 2013; Eteokleous, Neophytou, Kolani & Christodoulou, 2020; Giang, Piatti & Mondada, 2019; Ponticorvo, Rubinacci, Marocco, Truglio, & Miglino, 2020; Sapounidis, Alimisis, 2021; Stewart, 2021). Especially increased is the interest for the contribution of robotics in the development of CT skills, which have been recognized as fundamental for all students. CT, as described by Wing (2011, 2017), “is the thought processes involved in formulating a problem and expressing its solution(s) in such a way that a computer—human or machine—can effectively carry out” and is highly important for controlling and managing cognitive activities in all disciplines (Wing, 2008).

Regarding the guidance provided by the teacher, researchers have focused on the impact that prompting strategies may have on students’ effective engagement in the deeper processing of the learning material. Certainly, it is not always clear what type of prompting, and what conditions, can maximize the benefits of this process. Many researchers agree that an effective prompting strategy is providing written answers. However, this type of student response (‘writing’) is also reported to be tiresome and boring for students, causing heavy workload (Anewalt, 2002; Atmatzidou et al., 2018; Papadopoulos, Demetriadis, Stamelos, & Tsoukalas, 2011).

In the light of above, this study investigates the effectiveness of prompting strategies that trigger students’ cognitive processing in ER activities. The focus is on the impact of different students’ response modality and the study explores how to achieve improved learning outcomes without the negative impact of imposing on students a heavy workload, as students are not always willing to write down and explicitly report their thoughts during problem solving activity.

Analytically, the study presents the implementation of ER activities conducted with the participation of 56 elementary school students. Students worked in small teams, guided by worksheets to solve authentic complex problems and were guided to develop CT skills based on a CT model that includes: abstraction, generalization, algorithm building, modularity and debugging. The students were distributed in three conditions employing different student response modality as follows: a) ‘Control condition’ -the baseline condition teams were prompted during problem solving without being mandatory for them to explicitly answer any of the questions during the activity. b) ‘Writing condition’ teams were asked to provide explicitly written answers, in order to describe, document, justify and advocate their suggested solution to the prompts, and c) ‘Selecting condition’ teams were asked to select the correct answer in closed-type questions relevant to the type of prompts presented to the other conditions. In the following, we present: a) the theoretical background of our work, and b) a study conducted in an elementary school that focuses on the development of computational thinking skills, and contrasts the impact of three different prompting strategies on response. The results provide encouraging evidence regarding the positive impact on the development of students’ computational thinking skills.

Key Terms in this Chapter

Algorithm: Algorithm is a step-by-step sequence of finite and unambiguous instructions for carrying out a process.

Modularity: The development of autonomous processes which encapsulate a set of often used commands that perform a specific function and might be used in the same or different problems.

Fading (Fade-Out): The fading strategy refers to decreasing the level of assistance or guidance needed to complete a task or activity. It is usually paired with prompts.

Generalization: Generalization is transferring a problem-solving process to a wide variety of problems.

Educational Robotics (ER): ER is a powerful, flexible, teaching and learning tool, encouraging students to construct and control robots using specific programming languages. They help students to turn from passive into active learners as they construct new knowledge and develop fundamental skills by collaborating with their peers and acting as researchers.

Abstraction: The process of creating something simple from something complicated by leaving out the irrelevant details, finding the relevant patterns, and separating ideas from tangible details.

Collaboration Script: They organize the collaboration process of two or more people, entities, or organizations working together to complete a task or achieve a goal. Specifically, the collaboration scripts organize the task into a sequence of phases, assigning student roles and tasks, and explicating the specificities of role-playing and task development. They aim to trigger peer interaction and to engage team members in fruitful and productive learning.

Computational Thinking (CT): CT is the thought processes involved in formulating a problem and expressing its solution(s) in such a way that a computer -human or machine- can effectively carry out. It is a fundamental skill for all individuals and its key components are analysis, algorithm, decomposition (breaking down into parts), patterns, abstraction, generalization, evaluation, and debugging.

Debugging: Debugging is the process of detecting and resolving existing and potential errors that prevent the correct operation of a system

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