Promoting Students’ Progress-Monitoring Behavior during Block-Based Programming

Marwan, Samiha; Shabrina, Preya; Milliken, Alex; Menezes, Ian; Cateté, Veronica; Price, Thomas W.; Barnes, Tiffany

doi:10.1145/3488042.3488064

Cited by 5 publications

(3 citation statements)

References 34 publications

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“…• evaluation of the tool's usefulness on a 5point Likert scale from "completely true" to "not at all true" ("LitterBox can help teachers to create good example programs. ") As the teachers in training had not used the tool previously, group Trmt also received a short video about its usage and how it works, as we knew from our pre-study and a related study with high school students [27] that explaining these aspects would avoid confusion.…”

Section: Participantsmentioning

confidence: 99%

“…Teachers in training perceived some missing functionality as negative (Table 5). While incorrect analyses such as false positives are a common problem of automated feedback tools and should therefore be explained beforehand to enable users to interpret automated hints [27], other criticised aspects are out of the scope of the LitterBox tool such as the analysis of semantics (Table 5): "Litterbox cannot judge, if my code makes sense semantically" (Trmt, P35). This is true as LitterBox is a static analysis tool that analyses the code patterns but not the output [6].…”

Section: Functionalitymentioning

confidence: 99%

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Exploring Programming Task Creation of Primary School Teachers in Training

Greifenstein

Heuer

2023

Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education v. 1

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Introducing computational thinking in primary school curricula implies that teachers have to prepare appropriate lesson material. Typically this includes creating programming tasks, which may overwhelm primary school teachers with lacking programming subject knowledge. Inadequate resulting example code may negatively affect learning, and students might adopt bad programming habits or misconceptions. To avoid this problem, automated program analysis tools have the potential to help scaffolding task creation processes. For example, static program analysis tools can automatically detect both good and bad code patterns, and provide hints on improving the code. To explore how teachers generally proceed when creating programming tasks, whether tool support can help, and how it is perceived by teachers, we performed a pre-study with 26 and a main study with 59 teachers in training and the LitterBox static analysis tool for Scratch. We find that teachers in training (1) often start with brainstorming thematic ideas rather than setting learning objectives, (2) write code before the task text, (3) give more hints in their task texts and create fewer bugs when supported by LitterBox, and (4) mention both positive aspects of the tool and suggestions for improvement. These findings provide an improved understanding of how to inform teacher training with respect to support needed by teachers when creating programming tasks. CCS CONCEPTS• Social and professional topics → Software engineering education; K-12 education; • Software and its engineering → Visual languages.

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Section: Participantsmentioning

confidence: 99%

Section: Functionalitymentioning

confidence: 99%

Exploring Programming Task Creation of Primary School Teachers in Training

Greifenstein

Heuer

2023

Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education v. 1

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“…Indeed, if the feedback from an automated assessment system targets specific parts of what is currently being worked on or even provides scaffolds [6], [7], students likely need to spend less time on determining the next steps. Naturally, such scaffolds can also be built within assignments themselves [8]- [11].…”

Section: Introductionmentioning

confidence: 99%

Piloting Natural Language Generation for Personalized Progress Feedback

Leppänen

Hellas

Leinonen

2022

2022 IEEE Frontiers in Education Conference (FIE)

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Full research paper-We describe the results of a pilot study wherein we applied simple natural language generation methods to produce automated feedback for students of an online course based on student high-level progress data. Experimenting with both personalized and non-personalized feedback, we show that such feedback can be easily produced given access to even rudimentary data regarding student assignment submissions and their correctness. Our results suggest that students perceive automatically generated feedback generally positively and believe it to be useful. Our results also indicate that minor personalization and stylistic alterations in the feedback can have meaningful effects on how the feedback is interacted with and perceived. In particular, we observe that personalized feedback is perceived as being slightly easier to understand and as being better aligned with their progress. Students also felt better about the personalized feedback in comparison to non-personalized feedback. We conclude that the automated generation of personalized textual feedback shows promise as a low-threshold way of increasing student satisfaction. Further research is needed to assess the effect of different types of automated personalized feedback on student performance and behavior.

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