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This article reviews literature on worked examples in the context of programming activities. We focus on two types of examples, namely, code-tracing and code-generation, because there is sufficient research on these to warrant a review. We synthesize key results according to themes that emerged from the review. This synthesis aims to provide practical guidance for educators and shed light on future research opportunities. While there is established work in some areas (e.g., dynamic code-tracing examples in the form of program visualization tools, utility of subgoals in code-generation examples, and incomplete examples in the form of Parsons puzzles), there are also gaps. Thus, the article concludes with directions for future work on examples in computer science education.

When students are first learning to program, they not only have to learn to write programs, but also how to trace them. Code tracing involves stepping through a program step-by-step, which helps to predict the output of the program and identify bugs. Students routinely struggle with this activity, as evidenced by prior work and our own experiences in the classroom. To address this, we designed a "Code Tracing" (CT)-Tutor. We varied the level of assistance provided in the tutor, based on (1) the interface scaffolding available during code tracing, and (2) instructional order, operationalized by when examples were provided, either before or after the corresponding problem was solved. We collected data by having participants use the tutor to solve code tracing problems (N = 97) and analyzed both learning outcomes and process data obtained by extracting features of interest from the log files. We used a multi-layered approach for the analysis, including standard inferential statistics and unsupervised learning to cluster students by their behaviors in the tutor. The results show that the optimal level of assistance for code tracing falls in the middle of the assistance spectrum included in the tutor, but also that there are individual differences in terms of optimal assistance for subgroups of individuals. Based on these results, we outline opportunities for future work around personalizing instruction for code tracing.

When students are solving problems they often turn to examples when they need assistance. Examples are helpful because they illustrate how a problem can be solved. However, when examples are very similar to the problems, students default to copying the example solutions, which hinders learning. To address this, prior work has investigated the effect of manipulating problem--example similarity, showing that learning can be increased by reducing the assistance provided by examples. We contribute to this literature by comparing two types of assistance mechanisms in the context of problem-solving activities: (1) "fade-out assistance," where initially the examples are similar to the problems but over time the problem--example similarity is reduced, and (2) "fade-in assistance" where the opposite is the case (initially the problem--example pairs have reduced similarity but the similarity is increased as more problems are solved). The fade-in assistance condition produced significantly higher learning gains than the fade-out condition and based on eye-tracking data, the fade-in group spent longer attending to the problem, particularly early on in the problem-solving session. Our conjecture that the fade-in group was engaged in more autonomous problem solving instead of copying was confirmed by exploratory analysis on a subset of the data showing that copying was initially reduced in the fade-in condition, as compared to high in the fade-out condition. Overall, our results highlight that initially struggling in a problem-solving activity results in more learning.

Learning to program requires perseverance, practice, and the mindset that programming skills are improved through these activities (i.e., that everyone has the potential to become good at programming). In contrast to an entity mindset, individuals with an incremental mindset believe that ability is malleable and can be improved with effort. Prior research shows that an incremental mindset can be promoted through interventions and that, as a result, individuals report increased belief in the value of effort. Although this is encouraging, the majority of research targets a general mindset, and so little work exists exploring the effect of this construct in the programming domain. The present study (N = 47) used a programming activity to test the effect of an incremental mindset intervention on participants' beliefs, effort, programming behaviors, and performance in an experimental study. The intervention was successful. Compared to the control group, the experimental group shifted significantly more toward an incremental mindset, which resulted in beneficial behaviors related to effort, namely higher time on task and more program creation and modification actions. These positive behaviors, however, did not translate to improvements in programming performance. We speculate the reason for this latter finding may be related to the need for additional domain-based support.

We investigated the impact of assistance on learning and affect during problem-solving activities with a computer tutor we built using the Cognitive Tutor Authoring Tools framework. The tutor delivered its primary form of assistance in the form of worked-out examples. We manipulated the level of assistance the examples in the tutor provided, by having similar problem-example pairs in one version of the tutor (high-assistance condition) and reduced similarity problem-example pairs in the other version (reduced-assistance condition). The reduced-assistance condition resulted in significantly higher learning, without increasing negative affect like frustration.

The question of what type of attention is associated with creative potential has predominantly been examined using divergent thinking tasks and without the benefits of cognitive neuroscience methods. To address this gap, we rely on a neural approach by indexing attention using the P50 ERP component. The P50 measures sensory gating, which is the brain's ability to filter out or inhibit responses to repeated stimuli. We analyze the relationships between neural responses to P50 and performance on two tasks designed to measure creative potential: a convergent task and a divergent task. While we did not find evidence of a relationship between attention and the originality component of divergent thinking, our results show that leaky attention was positively associated with the fluency component of divergent thinking but negatively associated with performance on the convergent thinking task. This result highlights that the relationship between attention and performance on creative tasks may depend on the type of task.

As computational thinking becomes increasingly important for children to learn, we must develop interfaces that leverage the ways that young children learn to provide opportunities for them to develop these skills. Active Learning Environments with Robotic Tangibles (ALERT) and Robopad, an analogous on-screen virtual spatial programming environment for educational Human Robot Interaction (HRI), have been developed. Evaluations of these in the context of free play and open-ended learning activities show that both systems afford opportunities for young children to engage in spatial programming, creating improvisational and sequential programs that mediate interactions between the environment, robots, and humans in responsive and creative ways. These systems demonstrate innovative opportunities for advancing mixed reality spatial programming activities as a form of HRI that fosters engaging seamless cyberlearning experiences, across formal and informal environments.

Creativity is commonly assessed using divergent thinking tasks, which measure the fluency, flexibility, originality, and elaboration of participant output on a variety of different tasks. This study assesses the degree to which creativity can be identified based on linguistic features of participants' language while completing collaborative divergent thinking tasks. To this end, 78 participants' conversational dialogs (i.e., 39 dyads) within a chat environment were collected while completing three open-ended problem-solving tasks. Expert raters scored the dialogs in terms of fluency, flexibility, elaboration, and originality, as well as three types of creative language (metaphor and simile, humor, and word play). Factor analyses indicated that these scores captured two main constructs (creativity and elaboration). The linguistic features of the participants' language (captured computationally using natural language processing tools) accounted for significant amounts of variation in both the creativity (R[superscript 2] = 0.640) and elaboration (R[superscript 2] = 0.550) scores within linear mixed effect (LME) models. These results highlight specific linguistic features that can be used to explain large amounts of variance in constructs related to creativity.

Idea generation is an important component of most major theories of writing. However, few studies have linked idea generation in writing samples to assessments of writing quality or examined links between linguistic features in a text and idea generation. This study uses human ratings of idea generation, such as "idea fluency, idea flexibility, idea originality", and "idea elaboration", to analyze the extent to which idea generation relates to human judgments of essay quality in a corpus of college student essays. In conjunction with this analysis, linguistic features extracted from the essays are used to develop a predictive model of idea generation to further understand relations between the language features in an essay and the idea generation scores assigned to that essay. The results indicate that essays rated as containing a greater number of ideas that were flexible, original, and elaborated were judged to be of higher quality. Two of these features (elaboration and originality) were significant predictors of essay quality scores in a regression analysis that explained 33% of the variance in human scores. The results also indicate that idea generation is strongly linked to language features in essays. Specifically, the use of unique multiword units, more difficult words, semantic but not lexical similarities between paragraphs, and fewer word repetitions explained 80% of the variance in human scores of idea generation. These results have implications for writing theories and writing practice.

This article describes research results based on multiple years of experimentation and real-world experience with an adaptive tutoring system named Wayang Outpost. The system represents a novel adaptive learning technology that has shown successful outcomes with thousands of students, and provided teachers with valuable information about students' mathematics performance. We define progress in three areas: improved student "cognition," "engagement," and "affect," and we attribute this improvement to specific components and interventions that are inherently "affective," "cognitive," and "metacognitive" in nature. For instance, improved student "cognitive outcomes" have been measured with pre-post tests and state standardized tests, and achieved due to personalization of content and math fluency training. Improved student "engagement" was achieved by supporting students' metacognition and motivation via affective learning companions and progress reports, measured via records of student gaming of the system. Student "affect" within the tutor was measured through sensors and student self-reports, and supported through affective learning companions and progress reports. Collectively, these studies elucidate a suite of effective strategies to support advanced personalized learning via an intelligent adaptive tutor that can be tailored to the individual needs, emotions, cognitive states, and metacognitive skills of learners.

A promising instructional approach corresponds to" learning by observing others learn" (i.e., by watching tutorial dialogue between a tutor and tutee). However, more work is needed to understand this approach's pedagogical utility. Thus, in 2 experiments we compared student learning from collaborative observation of dialogue with 2 other instructional contexts: 1-on-1 human tutoring and collaborative observation of monologue. In Study 1 (N = 50), there was no significant difference in learning outcomes between the dialogue and tutoring conditions, while the dialogue condition was superior to the monologue condition. Study 2 (N = 40), which involved a younger population than in Study 1, did not replicate these results, in that students learned less from observing dialogue than from being tutored, and there was no significant difference between the dialogue and monologue conditions. To shed light on our results, we analyzed the verbal data collected during the 2 experiments. This analysis showed that in Study 1, the dialogue observers generated more substantive contributions than did the monologue observers. In contrast, in Study 2 there was no significant difference between the observers in terms of substantive contributions; moreover, the total number of contributions was modest, which may have hindered observer learning in that study. In general, our findings suggest that collaboratively observing tutorial dialogue is a promising learning paradigm, but more work is needed to understand how to support young students to effectively learn in this paradigm.

We provide evidence of persistent gender effects for students using advanced adaptive technology while learning mathematics. This technology improves each gender's learning and affective predispositions toward mathematics, but specific features in the software help either female or male students. Gender differences were seen in the students' style of use of the system, motivational goals, affective needs, and cognitive/affective benefits, as well as the impact of affective interventions involving pedagogical agents. We describe 4 studies, with hundreds of students in public schools over several years, which suggest that technology responses should probably be customized to each gender. This article shows differential results before, during, and after the use of adaptive tutoring software, indicating that digital tutoring systems can be an important supplement to mathematics classrooms but that male and female students should be addressed differently. Female students were more receptive than male students to seeking and accepting help provided by the tutoring system and to spending time seeing the hints; thus, they had a consistent general trend to benefit more from it, especially when affective learning companions were present. In addition, female students expressed positively valenced emotions most often and exhibited more productive behaviors when exposed to female characters; these affective pedagogical agents encouraged effort and perseverance. This was not the case for male students, who had more positive outcomes when no learning companion was present and their worst affective and cognitive outcomes when the female character was present.

Although worked-out examples play a key role in cognitive skill acquisition, research demonstrates that students have various levels of meta-cognitive abilities for using examples effectively. The Example Analogy (EA)-Coach is an Intelligent Tutoring System that provides adaptive support to foster meta-cognitive behaviors relevant to a specific type of example-based learning known as "analogical problem solving" ("APS"), i.e., using examples to aid problem solving. To encourage the target meta-cognitive behaviors, the EA-Coach provides multiple levels of scaffolding, including an innovative example-selection mechanism that chooses examples with the best potential to trigger learning and enable problem solving for a given student. To find such examples, the mechanism relies on our novel classification of problem/example differences and associated hypotheses regarding their impact on the APS process. Here, we focus on describing (1) how the overall design of the EA-Coach in general, and the example-selection mechanism in particular, evolved from cognitive science research on APS; (2) our pilot evaluations and the controlled laboratory study we conducted to validate the tutor's pedagogical utility. Our results show that the EA-Coach fosters meta-cognitive behaviors needed for effective learning during APS, while helping students achieve problem-solving success. (Contains 2 footnotes, 3 tables, and 11 figures.)