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Educational Data Mining and Learning Analytics in Programming: Literature Review and Case Studies
TLDR
An overview of the body of knowledge regarding the use of educational data mining and learning analytics focused on the teaching and learning of programming is provided and a novel taxonomy to analyse replicating studies is introduced.
Data-Driven Hint Generation in Vast Solution Spaces: a Self-Improving Python Programming Tutor
TLDR
The results show that ITAP is capable of producing hints for almost any given state after being given only a single reference solution, and that it can improve its performance by collecting data over time.
Automatic Generation of Programming Feedback; A Data-Driven Approach
TLDR
A data-driven approach for automatic feedback generation which utilizes the program solution space to predict where a student is located within the set of many possible learning progressions and what their next steps should be is proposed.
Automating Hint Generation with Solution Space Path Construction
TLDR
It is shown how solution paths can be constructed from these abstract states that go beyond the paths directly observed in the data, and described a domain-independent algorithm that can automate hint generation through use of these paths.
A Canonicalizing Model for Building Programming Tutors
TLDR
This work has constructed a language-independent canonicalized model for programming solutions that allows for much greater overlap across different students than a basic text model, which enables more self-sustaining hint generation methods in programming tutors.
Automated Data-Driven Hint Generation for Learning Programming
TLDR
Intelligent tutoring systems can provide personalized feedback to students automatically, but they can take large amounts of time and expert knowledge to build, especially when determining how to give students hints.
Towards improving programming habits to create better computer science course outcomes
TLDR
It is found that students who start earlier tend to earn better scores, which is consistent with the findings of other researchers, and how students use release tokens is evaluated, a novel mechanism that provides feedback to students without giving away the code for the test cases used for grading.
ProgSnap2: A Flexible Format for Programming Process Data
TLDR
This paper investigated three metrics designed to quantify students' difficulty with compiler errors - the Error Quotient, Repeated Error Density and Watwin score - and compared their distributions and ability to predict students' performance.
Learning Curve Analysis for Programming: Which Concepts do Students Struggle With?
TLDR
This paper investigates programming data by using learning Curve analysis to determine which programming elements students struggle with the most when learning in Python, and extends the traditional use of learning curve analysis to include less structured data.
CloudCoder: building a community for creating, assigning, evaluating and sharing programming exercises (abstract only)
TLDR
CloudCoder is an effort to build a community based on an open-source programming exercise system tightly integrated with a repository of freely-redistributable programming exercises written and used by members of the community.
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