Improving Engagement in Program Construction Examples for Learning Python Programming

Hosseini, Roya; Akhüseyinoğlu, Kamil; Brusilovsky, Peter; Malmi, Lauri; Pollari-Malmi, Kerttu; Schunn, Christian D.; Sirkiä, Teemu

doi:10.1007/s40593-020-00197-0

Cited by 39 publications

(15 citation statements)

References 71 publications

(49 reference statements)

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“…For example, in MOOCs, engagement is frequently assessed by the fraction of watched videos, the number of attempted quizzes, or the number of posts to a discussion forum (Anderson et al, 2014 ; Davis et al, 2017 ; Crues et al, 2018 ). Similarly, online practice systems generally measure learner engagement through the amount of voluntary practice with worked examples and problems (Denny et al, 2018 ; Hosseini et al, 2020 ). Following this trend, we approximated engagement as the amount of students' non-mandatory work with different learning activities available in the practice system.…”

Section: Methodsmentioning

confidence: 99%

Exploring Behavioral Patterns for Data-Driven Modeling of Learners' Individual Differences

Akhüseyinoğlu

Brusilovsky

2022

Front. Artif. Intell.

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Educational data mining research has demonstrated that the large volume of learning data collected by modern e-learning systems could be used to recognize student behavior patterns and group students into cohorts with similar behavior. However, few attempts have been done to connect and compare behavioral patterns with known dimensions of individual differences. To what extent learner behavior is defined by known individual differences? Which of them could be a better predictor of learner engagement and performance? Could we use behavior patterns to build a data-driven model of individual differences that could be more useful for predicting critical outcomes of the learning process than traditional models? Our paper attempts to answer these questions using a large volume of learner data collected in an online practice system. We apply a sequential pattern mining approach to build individual models of learner practice behavior and reveal latent student subgroups that exhibit considerably different practice behavior. Using these models we explored the connections between learner behavior and both, the incoming and outgoing parameters of the learning process. Among incoming parameters we examined traditionally collected individual differences such as self-esteem, gender, and knowledge monitoring skills. We also attempted to bridge the gap between cluster-based behavior pattern models and traditional scale-based models of individual differences by quantifying learner behavior on a latent data-driven scale. Our research shows that this data-driven model of individual differences performs significantly better than traditional models of individual differences in predicting important parameters of the learning process, such as performance and engagement.

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Section: Methodsmentioning

confidence: 99%

Exploring Behavioral Patterns for Data-Driven Modeling of Learners' Individual Differences

Akhüseyinoğlu

Brusilovsky

2022

Front. Artif. Intell.

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“…( 2) Examples (MasteryGrids-PCLab). For each of the proposed topics in the system, there are established examples, called Program Construction EXamples (PCEX) [45]. The idea is to support students in acquiring program construction skills through a new type of smart and interactive content.…”

Section: The Learning Content Provided In the Systemmentioning

confidence: 99%

Adaptive Assessment and Content Recommendation in Online Programming Courses: On the Use of Elo-rating

Vesin

Mangaroska

Akhüseyinoğlu

et al. 2022

ACM Trans. Comput. Educ.

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Online learning systems should support students preparedness for professional practice, by equipping them with the necessary skills while keeping them engaged and active. In that regard, the development of online learning systems that support students’ development and engagement with programming is a challenging process. Early career computer science professionals are required not only to understand and master numerous programming concepts, but to efficiently learn how to apply them in different contexts. A prerequisite for an effective and engaging learning process is the existence of adaptive and flexible learning environments that are beneficial for both, students and teachers. Students can benefit from personalized content adapted to their individual goals, knowledge, and needs; while teachers can be relieved from the pressure to uniformly and promptly evaluate hundreds of student assignments. This study proposes and puts into practice a method for evaluating learning content difficulty and students’ knowledge proficiency utilizing a modified Elo-rating method. The proposed method effectively pairs learning content difficulty with students’ proficiency, and creates personalized recommendations based on the generated ratings. The method was implemented in a programming tutoring system and tested with interactive learning content for object oriented-programming. By collecting quantitative and qualitative data from students who used the system for one semester, the findings reveal that the proposed method can generate recommendations that are relevant to students and has the potential to assist teachers in grading students by providing a more holistic understanding of their progress over time.

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“…The importance on interactive engagement such as viewing, responding, changing or constructing is highlighted in [5]. Engagement is defined as the extent of a student's active involvement in a learning activity.…”

Section: Background 21 Engagementmentioning

confidence: 99%

“…Deep engagement improves learning. Authors of [5] show that time spent on a learning task is positively correlated with learning and better performance. Engagement is enhanced with engaging programming examples.…”

Section: Background 21 Engagementmentioning

confidence: 99%

Feedback and Engagement on an Introductory Programming Module

Grawemeyer¹,

Halloran²,

England³

et al. 2022

Preprint

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We ran a study on engagement and achievement for a first year undergraduate programming module which used an online learning environment containing tasks which generate automated feedback. Students could also access human feedback from traditional labs. We gathered quantitative data on engagement and achievement which allowed us to split the cohort into 6 groups. We then ran interviews with students after the end of the module to produce qualitative data on perceptions of what feedback is, how useful it is, the uses made of it, and how it bears on engagement. A general finding was that human and automated feedback are different but complementary. However there are different feedback needs by group. Our findings imply: (1) that a blended human-automated feedback approach improves engagement; and (2) that this approach needs to be differentiated according to type of student. We give implications for the design of feedback for programming modules.

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Improving Engagement in Program Construction Examples for Learning Python Programming

Cited by 39 publications

References 71 publications

Exploring Behavioral Patterns for Data-Driven Modeling of Learners' Individual Differences

Exploring Behavioral Patterns for Data-Driven Modeling of Learners' Individual Differences

Adaptive Assessment and Content Recommendation in Online Programming Courses: On the Use of Elo-rating

Feedback and Engagement on an Introductory Programming Module

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