Employing a Think-Aloud Approach to Assess Young Learners' Computational Thinking
Yicheng Pan · Proceedings of the 2019 AERA Annual Meeting · 2019
Computational Thinking (CT) involves skills that help youth analyze and solve real-world problems drawing on computer science (CS) principles (Wing, 2006).Programming is one way of promoting CT, though arguably CS is broader than programming and CT is even broader than CS (NRC, 2010).Nevertheless, programming and CT are closely related and with strong ties to CS (Shute, Sun, & Asbell-Clarke, 2017).In recent years, there has been increased attention in helping all students acquire CT knowledge and skills, particularly through programming initiatives.Yet, assessing or measuring CT development is a challenge, because assessments of CT remain underdeveloped and under-researched (Yadav et al., 2015).Recent efforts to address this challenge include the development of evaluative tools such as written tests or surveys (e.g., Litts, Kafai, Lui, Walker & Widman, 2017), analysis of students computational artifacts (Brennan & Resnick, 2012), interviews (Barron, Wise, & Martin, 2013), opportunities for students to engage in scenarios (Brennan & Resnick, 2012), and web-based assessment tasks that integrate CT with disciplinary content (Basu, McElhaney, Grover, Harris, & Biswas, 2018).Further, given the widespread use of block-based programming environments among young learners (e.g., Scratch), some authors delineated assessments and rubrics for capturing students' learning of computational content by examining the number and use of Scratch blocks (e.g., Proctor & Blikstein, 2018).One factor that makes CT difficult to measure is that it focuses on the thought process, not on the end product or artifact alone (Mueller et al, 2017).Ahonen and Kankaanranta (2015) recognized that "think-aloud" could be an effective way to assess students' CT process, such as breaking down problems and developing algorithms.Specifically, think-alouds can be implemented in two ways: students describe and unveil their metacognitive process of solving a problem by thinking aloud while completing tasks in a concurrent method or after they complete the task in a retrospective method (Ericsson & Simon, 1993).Taking into account the long history of think-alouds in other disciplines, Mueller et al. (2017) advocate their use in the assessment of students' CT.To collect think-aloud data, Mueller et al. proposed a set of teacher verbal protocols to help in the assessment of students' CT process.These protocols include guiding questions for different CT constructs, including computational concepts, practices and perspectives, based on an assessment framework informed by Brennan and Resnick (2012) and Csizmadia et al. (2015) (see Table 1).By asking questions as a communication tool, teachers are able to understand students' CT ability during conversations and pinpoint strengths and weaknesses.***Insert Table 1*** While the framework proposed by Mueller et al. (2017) holds promise for assessing students' development of CT, they have not provided empirical evidence on student outcomes.In