Investigating students' computational problem-solving processes using Hidden Markov Model and video analysis

Tongxi Liu; Wei Yan

Investigating students' computational problem-solving processes using Hidden Markov Model and video analysis

Research output: Contribution to conference › Conference paper › peer-review

Abstract

Understanding students' problem-solving processes helps researchers and instructors provide appropriate and timely interventions in game-based learning environments. In this study, we aim to explore how students engage in problem-solving and identify potential moments of struggle in a puzzle-based computational thinking game. We first applied Hidden Markov Model to capture problem-solving transitions and located potential moments. We then employed video analysis to uncover how and why students struggled during their problem-solving processes.

Original language	English
Publication status	Published - 16 Oct 2023
Event	46th AECT International Convention 2023 - DoubleTree by Hilton at the Entrance to Universal Orlando, Virtual, Orlando, United States Duration: 15 Oct 2023 → 19 Oct 2023 https://convention.aect.org/ (Link to conference website) https://aect.org/aects_annual_convention_proce.php (Link to conference proceedings )

Conference

Conference	46th AECT International Convention 2023
Country/Territory	United States
City	Orlando
Period	15/10/23 → 19/10/23
Internet address	https://convention.aect.org/ (Link to conference website) https://aect.org/aects_annual_convention_proce.php (Link to conference proceedings )

Cite this

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title = "Investigating students' computational problem-solving processes using Hidden Markov Model and video analysis",

abstract = "Understanding students' problem-solving processes helps researchers and instructors provide appropriate and timely interventions in game-based learning environments. In this study, we aim to explore how students engage in problem-solving and identify potential moments of struggle in a puzzle-based computational thinking game. We first applied Hidden Markov Model to capture problem-solving transitions and located potential moments. We then employed video analysis to uncover how and why students struggled during their problem-solving processes.",

author = "Tongxi Liu and Wei Yan",

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AU - Yan, Wei

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N2 - Understanding students' problem-solving processes helps researchers and instructors provide appropriate and timely interventions in game-based learning environments. In this study, we aim to explore how students engage in problem-solving and identify potential moments of struggle in a puzzle-based computational thinking game. We first applied Hidden Markov Model to capture problem-solving transitions and located potential moments. We then employed video analysis to uncover how and why students struggled during their problem-solving processes.

AB - Understanding students' problem-solving processes helps researchers and instructors provide appropriate and timely interventions in game-based learning environments. In this study, we aim to explore how students engage in problem-solving and identify potential moments of struggle in a puzzle-based computational thinking game. We first applied Hidden Markov Model to capture problem-solving transitions and located potential moments. We then employed video analysis to uncover how and why students struggled during their problem-solving processes.

UR - https://convention2.allacademic.com/one/aect/aect23/index.php?cmd=Online+Program+View+Paper&selected_paper_id=2062664&PHPSESSID=nd7amtgkjddrppg8ql8bh5gkdl

M3 - Conference paper

T2 - 46th AECT International Convention 2023

Y2 - 15 October 2023 through 19 October 2023

ER -

Investigating students' computational problem-solving processes using Hidden Markov Model and video analysis

Abstract

Conference

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