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The Computer Science Teachers Association (CSTA) K-12 Computer Science Standards identify ‘Algorithms and Programming’ as a key CS concept across all grade bands that encompasses sub-concepts such as algorithms, decomposition, variables, and control structures. Previous studies have shown that algorithms and programming concepts often pose challenges for novice programmers, and that instruction in these areas is often superficial. We developed formative assessment tasks to investigate middle school students’ understanding of four CS standards related to algorithms and programming and collected responses from over 100 students associated with five different teachers. We found that students demonstrated a limited understanding of the standards. These findings contribute to the growing literature on middle school students’ understanding of algorithms and programming, and provide insights that can inform CS teacher development, instruction, and curriculum design.
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This content will become publicly available on July 26, 2026
LearnLens: An AI-Enhanced Dashboard to Support Teachers in Open-Ended Classrooms
Exploratory learning environments (ELEs), such as simulation-based platforms and open-ended science curricula, promote hands-on exploration and problem-solving but make it difficult for teachers to gain timely insights into students' conceptual understanding. This paper presents LearnLens, a generative AI (GenAI)-enhanced teacher-facing dashboard designed to support problem-based instruction in middle school science. LearnLens processes students' open-ended responses from digital assessments to provide various insights, including sample responses, word clouds, bar charts, and AI-generated summaries. These features elucidate students' thinking, enabling teachers to adjust their instruction based on emerging patterns of understanding. The dashboard was informed by teacher input during professional development sessions and implemented within a middle school Earth science curriculum. We report insights from teacher interviews that highlight the dashboard's usability and potential to guide teachers' instruction in the classroom.
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- Award ID(s):
- 2327708
- PAR ID:
- 10650779
- Editor(s):
- Mavrikis, M; Lalle, S; Azevedo, R; Biswas, G; Roll, I
- Publisher / Repository:
- Workshop on Advances in Artificial Intelligence for Exploratory Learning (AI4EXL) (https://transeet.eu/ai4exl/)
- Date Published:
- Subject(s) / Keyword(s):
- GenAI Teacher-facing Dashboard Open-Ended Learning Environment LLM AI in Education Science Education
- Format(s):
- Medium: X
- Location:
- https://arxiv.org/abs/2509.10582
- Sponsoring Org:
- National Science Foundation
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