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Code tracing involves simulating at a high level the steps a computer takes when it executes a computer program. This is a fundamental skill needed for programming activities, but one that novices find challenging. Thus, work is needed on how to support novice programmers in this activity. We conducted an experimental study with university students (N = 56) learning to code trace in two conditions, namely peer tutoring and solo code tracing. Our primary outcome variable was learning. However, since how students feel is also an important factor in educational settings, we also measured student emotion. Contrary to prior work in other domains, there was no significant benefit of peer tutoring and self-reported levels of emotion were similar in the two conditions; Bayesian statistics provided evidence for the null model in the majority of cases.

Computer programs correspond to instructions that tell a computer how to perform a task. Because computers do not understand human languages, programs have to be written in a specialized programming language. Such languages have unique syntax and semantics that on the surface appear quite different from human languages. To foster understanding what a program means, novices need to translate the programming language instructions into a familiar human language. We developed a tutoring system that provides translation activities, from the language Python into plain English, and feedback on student answers. One version of the tutoring system provides open-ended input boxes that students type translations into. A second version instead asks students to generate answers to the translation using drop-down menus. The latter provides more scaffolding but also may encourage shallow behaviors such as guessing and checking. To shed light on how these designs influence learning, we evaluate them with novice programmers. Here, we report on the results of the evaluation and next steps.

A notional machine is an abstract representation of how a computer works. Prior research shows that students often form incomplete or inaccurate mental models of the notional machine, reflecting misunderstandings and causing programming errors. To date, however, there is very little work on how to help students acquire accurate models of the notional machine. To fill this gap, this research focuses on novice programmers and experimentally investigates the impact of direct instruction of the notional machine compared to standard instruction that does not include the notional machine. The instruction is embedded in an online computer tutor that we implemented and compared to a version of the tutor that does not refer to the notional machine but otherwise includes the same materials. In particular, both tutor versions include instructional videos followed by self-explanation prompts. We will present data from an evaluation of both tutor versions.

A fundamental skill needed to learn to program is code tracing, which involves simulating the steps a computer takes when executing a program to predict how it will behave and what output it. While code tracing helps students understand and write programs, students find this activity challenging. However, to date, little work exists on how to best support novice programmers in this activity. To address this gap, the high-level goal of our research is to investigates the utility of peer-tutoring for learning to trace programs. In our prior work, we conducted a preliminary case study exploring qualitative features of peer tutoring interactions. As the next step, we conducted an experimental study with two conditions: code tracing in a peer tutoring context vs. code-tracing alone. We present results from this study, focusing on quantitative results related to learning outcomes in each condition.

Programming is a complex problem-solving domain, requiring the coordination of different types of knowledge and skills. The present study investigates expert and novice programming problem solving by analyzing talk-aloud transcripts and the code generated. Based on this analysis a set of basic goal and step components used by novice and expert programmers are identified, which will inform on the generation of cognitive models in the next phase of this research.

Productive failure (PF) is a learning paradigm that reverses the standard order of instruction by asking students to solve problems prior to instruction. This paradigm has been shown to be effective for fostering student learning. To date, however, the role of student emotion in productive failure has not been investigated. In other paradigms, there is some evidence that failure elicits negative emotions and that these emotions can interfere with learning. This leads to a conundrum given productive failure’s positive effect on learning. To shed light on this, we report on results from a study (N = 48) in the productive failure paradigm. For the analysis, we used a mixed-methods approach to investigate the distribution of emotions in productive failure, how these changed across different instructional activities, and the relation between emotions and posttest performance.

Learning to program is challenging, because it involves novel skills. In contrast to the majority of work focusing on code generation, we target the skill of code tracing. Code tracing involves simulating the high-level actions a computer takes when it executes the program, including the flow of execution through it and how variable values change as a result. Code tracing supports program comprehension and generation. However, many students do not code trace effectively. Our project investigates the utility of peer tutoring for the learning of code tracing. While this approach remains untested in this context, it has successfully been used in other domains to improve student learning. We will present qualitative data from a case study involving students tutoring each other to code trace. This work is the first step in the context of a broader project focused on identifying ways to help students learn to code.

The goal of the present study was to use content analysis to gain insight into the process of problem-solving of novice and expert programmers. While classic work on programmers identifies goals / plans as key constructs needed to code, there is relatively little work using protocol analysis. We recruited 7 expert and 12 novice rogrammers who completed up to 3 brief programming problems while providing a talk-aloud of their inner problem solving process. Based on analysis of the transcriptions of this talk aloud data, we identified the goals and steps used, as well as the broad differences between experts and novices in their problem solving process. These differences were formalized into python ACT-R models, and model output was compared to programs written by human participants.

A mental model is an internal representation that explains how something works. Mental model construction is facilitated by self-explanation, the active generation of explanations for oneself. The overarching goal of this research is to empirically investigate the utility of self-explanation for developing mental models when learning to program. Programming is notoriously challenging and, despite evidence of the importance of mental models for learning, little work has focused on mental models of students learning how to program. They need correct mental models of the notional machine, an abstraction of the steps taken by a computer as it processes a program. Because students do not spontaneously self-explain, we are using a user-centered approach to design a computer tutor to prompt for self-explanation about the notional machine. Here, we present qualitative results on students’ interactions with an initial version of the tutor, including the form of their self-explanations and corresponding mental models.

It is established that examples are beneficial for learning, but are certain types of examples more helpful than others?Erroneous examples include errors that students are asked to correct, something that can be helpful in addressing mis-conceptions. One domain that is vulnerable to misconceptions is fraction arithmetic. In the present study, undergraduatestudents solved fraction problems using a tutoring system we designed. Some participants worked with the Erroneous-Example tutor, which supplemented problems with erroneous examples, while other participants worked with a traditionalProblem-Solving tutor that did not include erroneous examples. To evaluate the impact of tutor type on learning andself-efficacy, we analyzed difference scores from pre-test to post-test. While overall participants significantly improvedtheir fraction knowledge and self-efficacy, there was no significant difference between the two groups. Bayesian analysesprovided evidence for the null model, i.e., that erroneous examples were not more beneficial than traditional problemsolving.

The intrinsic integration hypothesis proposes that using coregame mechanisms to teach learning material makeseducational games more fun to play and better for learning.Our study tests the intrinsic integration hypothesis with twoeducational versions of Battleship that were designed for thisexperiment, in the domain of complex numbers. We examinethe learning gains and motivation of 58 participants whointeracted with either the intrinsically-integrated orextrinsically-integrated version of the game. Our resultscontradict previous findings supporting the intrinsicintegration hypothesis: participants reported similar levels ofmotivation from both versions of the game and participantswho interacted with the extrinsically-integrated versionlearned significantly more as measured by pretest to posttestgains. This work contributes empirical data to the debateconcerning intrinsic integration, and it highlights the need foradditional studies exploring the integration of learningmaterial into educational games.

While there is substantial evidence showing that assistanceprovided to students during problem-solving activitiesinfluences learning outcomes, it is not yet clear how to bestdesign educational technologies to maximize learning throughvarious types of assistance. One common type of assistancecorresponds to hints delivered by an educational technology.To date, however, there is little research on the impact ofdifferent types of hints, including high-level hints vs. specificbottom-out hints. Our research takes a step in filling this gap,through an experimental study with an intelligent tutoringsystem we implemented in the domain of algebra (N = 50).We did not find evidence that the type of hint, high level vs.bottom out, influenced learning, with both types of hintsproducing similar outcomes. We did, however, find supportfor the conclusion that the number of hints accessed interactedwith the type of hint to influence learning, and specifically,that accessing more hints was correlated with learning butonly in the high-level hint condition.

Whereas first language (L1) research has demonstrated thatperceptions of creative ability are influenced by the complexityand diversity of language used to answer verbal tests ofcreativity, relatively little is known about the linguisticcomponents of bilingual creative task performance. In thisstudy, we analyze written transcripts of speech produced by466 Japanese learners of English produced during a creativenarrative task for features related to linguistic and cognitivedimensions of creativity. Then, we extract various linguisticfeatures and test whether these features can predict humanperceptions of creativity for the transcripts. Unlike L1 data,results suggest text length and L2 proficiency comprise themost parsimonious explanation of creativity scores in this L2data. At the same time, linguistic features related to positivesentiment explained a significant yet small amount ofadditional variance in perceptions of creativity, suggestingtexts with more positive language were perceived to be morecreative.

When students solve problems with access to examples show-ing worked out solutions, they often resort to shallow methodslike copying that do not result in learning. An open questionis therefore how to encourage deeper processing in this type ofinstructional context. To address this question, in the presentstudy, we investigate the impact of manipulating problem-example similarity over the course of a problem-solving ses-sion in several ways, including faded assistance (high to lowsimilarity), reverse faded assistance (low to high similarity),and a control group with high, constant assistance. We foundthat the reverse faded assistance condition resulted in the great-est learning gains. We analyzed the gaze behaviours to shedlight on this finding and found that participants in this condi-tion focused significantly more on the problem solution, sug-gesting more cognitive processing during problem solving thanin the other conditions.

The Iowa Gambling Task (IGT) is an established toolused for evaluating the role of emotional learning underconditions of uncertainty. To date, however, themajority of studies have not explicitly manipulated theemotional content within the IGT or examined the effectof doing so on different populations. We address thisgap in the present study, focusing our analysis on twogroups: low vs. high psychopathy individuals insubclinical populations. Our findings demonstrate thatemotional content boosted learning for the high but notthe low psychopathy group.

People report solving division problems by mentally recastingdivision problems as multiplication (e.g., 72 ÷ 8 à 8 × [?] =72). Mediation of division by multiplication occurs mainly onlarger problems. Eye tracking data was used to determinewhether patterns of gaze durations on division problemsprovided support for mediation. Adults solved divisionproblems in two formats: traditional (e.g., 72 ÷ 8 = [ ]) andrecasted (e.g. 72 = 8 × [ ]). Processing of individual problemelements was compared across formats. Results providesupport for mediation. Processing patterns for traditionally-formatted problems were more similar to those for traditionaldivision in earlier work (72 ÷ 8) whereas problems in recastedformat (72 = 8 × [ ]) were more similar to patterns foundwhen participants solved multiplication problems (e.g., 8 × 9).These findings provide a novel source of support fordifferential processing of problems across presentationformats.