Factors Influencing Students' Help-Seeking Behavior while Programming with Human and Computer Tutors

Price, Thomas W.; Liu, Zhongxiu; Cateté, Veronica; Barnes, Tiffany

doi:10.1145/3105726.3106179

Cited by 46 publications

(18 citation statements)

References 37 publications

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“…In this paper, we evaluate the effects of feedback that is generated automatically by static analysis. Past research on automated hint generation has mainly considered the problem of providing hints on what should be the next step in solving programming assignments [20,23,25,30,32] or open ended programming tasks [19,21] and how novices seek help in these systems [1,15,16,22]. An exception is the work by Gusukuma et al [6], who showed that feedback delivery on mistakes that anticipate possible misconceptions generally leads to favourable results, and that showing such hints does not harm transfer to new tasks.…”

Section: Feedback On Codementioning

confidence: 99%

Effects of Hints on Debugging Scratch Programs: An Empirical Study with Primary School Teachers in Training

Greifenstein

Obermueller

Wasmeier

et al. 2021

The 16th Workshop in Primary and Secondary Computing Education

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Bugs in learners' programs are often the result of fundamental misconceptions. Teachers frequently face the challenge of first having to understand such bugs, and then suggest ways to fix them. In order to enable teachers to do so effectively and efficiently, it is desirable to support them in recognising and fixing bugs. Misconceptions often lead to recurring patterns of similar bugs, enabling automated tools to provide this support in terms of hints on occurrences of common bug patterns. In this paper, we investigate to what extent the hints improve the effectiveness and efficiency of teachers in debugging learners' programs using a cohort of 163 primary school teachers in training, tasked to correct buggy Scratch programs, with and without hints on bug patterns. Our experiment suggests that automatically generated hints can reduce the effort of finding and fixing bugs from 8.66 to 5.24 minutes, while increasing the effectiveness by 34% more correct solutions. While this improvement is convincing, arguably teachers in training might first need to learn debugging "the hard way" to not miss the opportunity to learn by relying on tools. We therefore investigate whether the use of hints during training affects their ability to recognise and fix bugs without hints. Our experiment provides no significant evidence that either learning to debug with hints or learning to debug "the hard way" leads to better learning effects. Overall, this suggests that bug patterns might be a useful concept to include in the curriculum for teachers in training, while tool-support to recognise these patterns is desirable for teachers in practice.

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Section: Feedback On Codementioning

confidence: 99%

Effects of Hints on Debugging Scratch Programs: An Empirical Study with Primary School Teachers in Training

Greifenstein

Obermueller

Wasmeier

et al. 2021

The 16th Workshop in Primary and Secondary Computing Education

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show abstract

“…The Hint Factory is a data-driven approach developed to generate Next-Step hints for students applying rules to solve open-ended problems in well-defined domains where there are multiple valid solutions [51,52]. New innovations in generating assistance from individual pieces of previous student's solutions have helped researchers extend the ideas of the Hint Factory to generate Next-Step hints for new domains including novice programming and linked list construction [17,49,43,8]. The Next-Step hints derived by the Hint Factory and used in our tutor are pointing hints that suggest a statement a student could derive using a single domain rule application.…”

Section: Next-step Hintsmentioning

confidence: 99%

“…Simply, Next-Step hints suggest the best proposition that can be derived in one step from the student's current proof. This corresponds to the next-step hints derived in all of our prior work using Hint Factory [8,7,52,51,14,15,43,44].…”

Section: Assistancementioning

confidence: 99%

“…The original Hint Factory opened a new field of data-driven hint generation that was first applied in tutors for logic [51,8], and then for linked lists [17]. More recently, the Hint Factory approach inspired new research in generating Next-Step hints for novice programming, based on generating assistance using pieces of previous student's solutions [17,49,43,8].…”

Section: Introductionmentioning

confidence: 99%

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The Impact of Looking Further Ahead: A Comparison of Two Data-driven Unsolicited Hint Types on Performance in an Intelligent Data-driven Logic Tutor

Cody¹,

Maniktala²,

Lytle³

et al. 2021

Preprint

Self Cite

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Research has shown assistance can provide many benefits to novices lacking the mental models needed for problem solving in a new domain. However, varying approaches to assistance, such as subgoals and next-step hints, have been implemented with mixed results. Next-Step hints are common in data-driven tutors due to their straightforward generation from historical student data, as well as research showing positive impacts on student learning. However, there is a lack of research exploring the possibility of extending datadriven methods to provide higher-level assistance. Therefore, we modified our data-driven Next-Step hint generator to provide Waypoints, hints that are a few steps ahead, representing problem-solving subgoals. We hypothesized that Waypoints would benefit students with high prior knowledge, and that Next-Step hints would most benefit students with lower prior knowledge. In this study, we investigated the influence of data-driven hint type, Waypoints versus Next-Step hints, on student learning in a logic proof tutoring system, Deep Thought, in a discrete mathematics course. We found that Next-Step hints were more beneficial for the majority of students in terms of time, efficiency, and accuracy on the posttest. However, higher totals of successfully used Waypoints were correlated with improvements in efficiency and time in the posttest. These results suggest that Waypoint hints could be beneficial, but more scaffolding may be needed to help students follow them.

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“…Help seeking is an important aspect of the learning process in allowing an individual to advance their understanding (Nelson-Le Gall, 1985), and develop their independent skill and abilities (Newman, 1994). Once an individual reaches an impasse -a situation where no progress is possible -the initiation of help seeking behavior can be valuable for allowing them to move beyond their impasse (Price et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

The Paradox of Help Seeking in the Entropy Mastermind Game

2020

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Research on Bayesian reasoning suggests that humans make good use of available information. Similarly, research on human information acquisition suggests that Optimal Experimental Design models predict human queries well. This perspective contrasts starkly with educational research on help seeking, which suggests that many students wait excessively long to ask for help, or even decline help when it is offered. We bring these lines of work together, exploring when people seek help as a function of problem state in the Entropy Mastermind code breaking game. The Entropy Mastermind game is a probabilistic version of the classic code breaking game, involving inductive, deductive and scientific reasoning. Whether help in the form of a hint was available was manipulated within subjects. Results showed that participants tended to ask for help late in the game play, often when they already had all the necessary information needed to crack the code. These results pose a challenge for some versions of Bayesian and Optimal Experimental Design frameworks. Possible theoretical frameworks to understand the results, including from computer science approaches to the Mastermind game, are considered.

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Factors Influencing Students' Help-Seeking Behavior while Programming with Human and Computer Tutors

Cited by 46 publications

References 37 publications

Effects of Hints on Debugging Scratch Programs: An Empirical Study with Primary School Teachers in Training

Effects of Hints on Debugging Scratch Programs: An Empirical Study with Primary School Teachers in Training

The Impact of Looking Further Ahead: A Comparison of Two Data-driven Unsolicited Hint Types on Performance in an Intelligent Data-driven Logic Tutor

The Paradox of Help Seeking in the Entropy Mastermind Game

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