A Framework for Using Hypothesis-Driven Approaches to Support Data-Driven Learning Analytics in Measuring Computational Thinking in Block-Based Programming Environments
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| Title: | A Framework for Using Hypothesis-Driven Approaches to Support Data-Driven Learning Analytics in Measuring Computational Thinking in Block-Based Programming Environments |
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| Language: | English |
| Authors: | Grover, Shuchi, Basu, Satabdi, Bienkowski, Marie, Eagle, Michael, Diana, Nicholas, Stamper, John |
| Source: | ACM Transactions on Computing Education. Aug 2017 17(3). |
| Availability: | Association for Computing Machinery. 2 Penn Plaza Suite 701, New York, NY 10121. Tel: 800-342-6626; Tel: 212-626-0500; Fax: 212-944-1318; e-mail: acmhelp@acm.org; Web site: http://toce.acm.org/ |
| Peer Reviewed: | Y |
| Page Count: | 25 |
| Publication Date: | 2017 |
| Sponsoring Agency: | National Science Foundation (NSF) |
| Contract Number: | IIS1522990 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Secondary Education High Schools |
| Descriptors: | Learning Analytics, Thinking Skills, Computation, Programming, Computer Science Education, High School Students, Student Evaluation |
| Geographic Terms: | California |
| DOI: | 10.1145/3105910 |
| ISSN: | 1946-6226 |
| Abstract: | Systematic endeavors to take computer science (CS) and computational thinking (CT) to scale in middle and high school classrooms are underway with curricula that emphasize the enactment of authentic CT skills, especially in the context of programming in block-based programming environments. There is, therefore, a growing need to measure students' learning of CT in the context of programming and also support all learners through this process of learning computational problem solving. The goal of this research is to explore hypothesis-driven approaches that can be combined with data-driven ones to better interpret student actions and processes in log data captured from block-based programming environments with the goal of measuring and assessing students' CT skills. Informed by past literature and based on our empirical work examining a dataset from the use of the Fairy Assessment in the Alice programming environment in middle schools, we present a framework that formalizes a process where a hypothesis-driven approach informed by Evidence-Centered Design effectively complements data-driven learning analytics in interpreting students' programming process and assessing CT in block-based programming environments. We apply the framework to the design of Alice tasks for high school CS to be used for measuring CT during programming. |
| Abstractor: | As Provided |
| Entry Date: | 2020 |
| Accession Number: | EJ1252440 |
| Database: | ERIC |
| Abstract: | Systematic endeavors to take computer science (CS) and computational thinking (CT) to scale in middle and high school classrooms are underway with curricula that emphasize the enactment of authentic CT skills, especially in the context of programming in block-based programming environments. There is, therefore, a growing need to measure students' learning of CT in the context of programming and also support all learners through this process of learning computational problem solving. The goal of this research is to explore hypothesis-driven approaches that can be combined with data-driven ones to better interpret student actions and processes in log data captured from block-based programming environments with the goal of measuring and assessing students' CT skills. Informed by past literature and based on our empirical work examining a dataset from the use of the Fairy Assessment in the Alice programming environment in middle schools, we present a framework that formalizes a process where a hypothesis-driven approach informed by Evidence-Centered Design effectively complements data-driven learning analytics in interpreting students' programming process and assessing CT in block-based programming environments. We apply the framework to the design of Alice tasks for high school CS to be used for measuring CT during programming. |
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| ISSN: | 1946-6226 |
| DOI: | 10.1145/3105910 |
