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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Aims and Objectives: This study investigates the effects of code-switching on vowel quality, pitch and duration among English-French bilinguals. Code-switching has been claimed to influence the morphology, syntax and lexicon, but not the phonology of the switched language. However, studies on voice-onset time have found subtle phonetic effects of code-switching, even though there are no categorical phonological effects. We investigate this further through the following three questions: (1) Are F1 and F2 influenced in the process of code-switching? (2) Are code-switched words hyper-articulated? (3) Does code-switching have an effect on vowel duration before voiced and voiceless consonants? Methodology: To address our research questions we relied on an insertional switching method where words from one language were inserted into carrier phrases of the other to simulate English-French code-switching environments. Bilingual speakers were recorded while they read code-switched sentences as well as sentences that did not involve code-switching, that is, monolingual sentences. Data and Analysis: The vowels of target words in the recorded utterances were compared - code-switched contexts against monolingual contexts - for vocalic duration, F0, F1 and F2. Findings/Conclusions: Like previous voice-onset time studies, our results indicate that code-switching does not shift the phonology to that of the embedded language. We did, however, find subtle lower level phonetic effects, especially in the French target words; we also found evidence of hyper-articulation in code-switched words. At the prosodic level, target switch-words approached the prosodic contours of the carrier phrases they are embedded in. Originality: The approach taken in this study is novel for its investigation of vowel properties instead of voice-onset time. Significance: This new approach to investigating code-switching adds to our understanding of how code-switching affects pronunciation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

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 (R2 = .640) and elaboration (R2 = .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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Information technology security management (ITSM) entails significant challenges, including the distribution of tasks and stakeholders across the organization, the need for security practitioners to cooperate with others, and technological complexity. We investigate the organizational processes in ITSM using qualitative analysis of interviews with ITSM practitioners. To account for the distributed nature of ITSM, we utilized and extended a distributed cognition framework that includes as key aspects the themes of cues and norms. We show how ITSM challenges foster under-use of cues and norms, which comprises a type of risk that may result in outcomes that are adverse to the organization's interests. Throughout, we use scenarios told by our participants to illustrate the various concepts related to cues and norms as well as ITSM breakdowns. [ABSTRACT FROM AUTHOR]

Students who exploit properties of an instructional system to make progress while avoiding learning are said to be 'gaming' the system. In order to investigate what causes gaming and how it impacts students, we analyzed log data from two Intelligent Tutoring Systems (ITS). The primary analyses focused on six college physics classes using the Andes ITS for homework and test preparation, starting with the research question: What is a better predictor of gaming, problem or student? To address this question, we developed a computational gaming detector for automatically labeling the Andes data, and applied several data mining techniques, including machine learning of Bayesian network parameters. Contrary to some prior findings, the analyses indicated that student was a better predictor of gaming than problem. This result was surprising, so we tested and confirmed it with log data from a second ITS (the Algebra Cognitive Tutor) and population (high school students). Given that student was more predictive of gaming than problem, subsequent analyses focused on how students gamed and in turn benefited (or not) from instructional features of the environment, as well as how gaming in general influenced problem solving and learning outcomes. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The article presents a study which examines the security incident response practices of information technology (IT) security practitioners as a diagnostic work process in Canada. The study notes the security incident response as a highly collaborative activity that may require such practitioners to develop their own tools to develop specific task. It also points out how the need of practitioners to rely on tacit knowledge and the usability issues with security tools complicate the diagnosis during incident response.

The Publisher regrets a spelling error in the family name of the third author. “Winslow Burelson” should be “Winslow Burleson”. [ABSTRACT FROM AUTHOR]