Computational thinking (CT) has emerged as a key topic of interest in K-12 education. Children that are exposed at an early age to STEM curriculum, such as computer programming and computational thinking, demonstrate fewer obstacles entering technical fields (Madill et al., 2007). Increased knowledge of programming and computation in early childhood is also associated with better problem solving, decision-making, basic number sense, language skills, and visual memory (Flannery et al., 2013). As a digital competence, coding is explicitly regarded as a key 21st Century Skill, as the "literacy of today," such that its acquisition is regarded as essential to sustain economic development and competitiveness (Bocconi et al., 2016). Therefore, the reliable evaluation of students' coding process data, in context of problem solving tasks that require CT, is of great importance. Prior research has analyzed overall action sequences or code snapshots, but has not interpreted student actions in context of a situation during the problem solving process -- i.e. while creating the solution. A more fine-grained analysis of coding process data is needed, where relevant actions are interpreted as a part of the student's problem solving process. We introduce a novel visualization approach for the analysis of coding process data. This approach has the following benefits: (a) It does not require the definition of process states; (b) It does not accumulate data (either across students or over time) and thus preserves the raw information aspect of the data; (c) It is goal-oriented, by being based on well-defined and measurable performance objectives; (d) It facilitates the definition of specific performance similarity measures for each performance objective (e.g. distance to optimal path or similarity to optimal event sequence), and thus facilitates scoring; (e) It is independent of sequence data length and thus enables time series analysis (e.g. frequency, pauses, etc.) (f) It can visualize each student's performance for each measure as a function of time; and (g) It can be used to inform the feature extraction process by facilitating pattern identification. We present our visualizations of student process data, collected using codeSpark Academy, which introduces children to programming and computational concepts (sequencing, parameters, loops, events, and conditionals) and combines carefully scaffolded puzzles aligned with the curriculum. Our findings clearly show groups of patterns that represent different strategies related to the computational thinking constructs abstraction, decomposition, generalization, modeling, algorithmic thinking, and evaluation.

The U.S. Navy is looking for ways to enhance recruitment efforts through a game-based environment that familiarizes potential recruits with the roles and responsibilities of enlisted Navy ratings, and determines players’ relative occupational interests therein. A bench-test approach based on pilot data was used to validate the game's design feasibility of gathering data about interest, based on player task choices across five represented ratings: Damage Controlman (DC), Fire Controlman (FC), Machinist's Mate (MM), Operations Specialist (OS), and Personnel Specialist (PS). A follow-on formative evaluation study is discussed where 65 high school students went through the game, completed a familiarity measure (pre- and post-gameplay), and completed interest measures (both in-game and out) that were compared with participant responses to the Navy's currently used occupational interest measure, Job Opportunities in the Navy (JOIN). Findings and implications for future research are presented.

Introduction: The need for teamwork training is well documented; however, teaching these skills is challenging given the logistics of assembling individual team members together to train in person. We designed 2 modes of screen-based simulation for training teamwork skills to assess whether interactivity with nonplayer characters was necessary for in-game performance gains or for player satisfaction with the experience. Methods: Mixed, randomized, repeated measures study with licensed healthcare providers block-stratified and randomized to evaluation-participant observes and evaluates the team player in 3 scenarios-and game play-participant is immersed as the leader in the same 3 scenarios. Teamwork construct scores (leadership, communication, situation monitoring, mutual support) from an ontology-based, Bayesian network assessment model were analyzed using mixed randomized repeated measures analyses of variance to compare performance, across scenarios and modes. Learning was measured by pretest and posttest quiz scores. User experience was evaluated using chi(2) analyses. Results: Among 166 recruited and randomized participants, 120 enrolled in the study and 109 had complete data for analysis. Mean composite teamwork Bayesian network scores improved for successive scenarios in both modes, with evaluation scores statistically higher than game play for every teamwork construct and scenario (r = 0.73, P = 0.000). Quiz scores improved from pretest to posttest (P = 0.004), but differences between modes were not significant. Conclusions: For training teamwork skills using screen-based simulation, interactivity of the player with the nonplayer characters is not necessary for in-game performance gains or for player satisfaction with the experience.

Supplemental digital content is available in the text. Introduction The need for teamwork training is well documented; however, teaching these skills is challenging given the logistics of assembling individual team members together to train in person. We designed 2 modes of screen-based simulation for training teamwork skills to assess whether interactivity with nonplayer characters was necessary for in-game performance gains or for player satisfaction with the experience. Methods Mixed, randomized, repeated measures study with licensed healthcare providers block-stratified and randomized to evaluation—participant observes and evaluates the team player in 3 scenarios—and game play—participant is immersed as the leader in the same 3 scenarios. Teamwork construct scores (leadership, communication, situation monitoring, mutual support) from an ontology-based, Bayesian network assessment model were analyzed using mixed randomized repeated measures analyses of variance to compare performance, across scenarios and modes. Learning was measured by pretest and posttest quiz scores. User experience was evaluated using χ2 analyses. Results Among 166 recruited and randomized participants, 120 enrolled in the study and 109 had complete data for analysis. Mean composite teamwork Bayesian network scores improved for successive scenarios in both modes, with evaluation scores statistically higher than game play for every teamwork construct and scenario (r = 0.73, P = 0.000). Quiz scores improved from pretest to posttest (P = 0.004), but differences between modes were not significant. Conclusions For training teamwork skills using screen-based simulation, interactivity of the player with the nonplayer characters is not necessary for in-game performance gains or for player satisfaction with the experience.

While speakers may disagree or have difficulty deciding the ordering of semantically similar adjectives such as icy and frosty, arranging adjectives on a linear scale is an intuitive task for most speakers of any language. Currently, there is no standard method or database to order gradable adjectives that share a common attribute, such as temperature or speed. Modeling how adjectives relate to one another semantically is an important aspect of language understanding. Applications of adjective intensity ordering include detecting scale differences in reviews and measuring similarity of larger segments of text. This paper discusses a novel approach that solves a system of linear equations constructed only from word dictionary definitions to compare the intensity of similar adjectives. We used Krippendorff's alpha to measure the agreement between a human standard and our algorithms' results on an ordinal scale and attained a score of 0.927 for temperature-, 0.652 for speed, and 0.673 for happiness-related adjectives. As a reference, Krippendorff inter-rater agreement was 0.927 for temperature, 0.816 for speed-related adjectives, and 0.849 for happiness-related adjectives.

Cognitive operations are supported by dynamically reconfiguring neural systems that integrate processing components widely distributed throughout the brain. The inter-neuronal connections that constitute these systems are powerfully shaped by environmental input. We evaluated the ability of computer-presented brain training games done in school to harness this neuroplastic potential and improve learning in an overall study sample of 583 second-grade children. Doing a 5-minute brain-training game immediately before math or reading curricular content games increased performance on the curricular content games. Doing three 20-minute brain training sessions per week for four months increased gains on school-administered math and reading achievement tests compared to control classes tested at the same times without intervening brain training. These results provide evidence of cognitive priming with immediate effects on learning, and longer-term brain training with far-transfer or generalized effects on academic achievement.

This book examines Thomas De Quincey's notion of the unconscious in the light of modern cognitive science and nineteenth-century science. It challenges Freudian theories as the default methodology in

The Web is making possible many advanced text-mining applications, such as news summarization, essay grading, question answering, semantic search and structured queries on corpora of Web documents. For many of such applications, statistical text-mining techniques are of limited effectiveness since they do not utilize the morphological structure of the text. On the other hand, many approaches use NLP-based techniques that parse the text into parse trees, and then use patterns to mine and analyze parse trees which are often unnecessarily complex. To reduce this complexity and ease the entire process of text mining, we propose a weighted-graph representation of text, called TextGraphs, which captures the grammatical and semantic relations between words and terms in the text. TextGraphs are generated using a new text mining framework which is the main focus of this paper. Our framework, SemScape, uses a statistical parser to generate few of the most probable parse trees for each sentence and employs a novel two-step pattern-based technique to extract from parse trees candidate terms and their grammatical relations. Moreover, SemScape resolves coreferences by a novel technique, generates domain-specific TextGraphs by consulting ontologies, and provides a SPARQL-like query language and an optimized engine for semantically querying and mining TextGraphs.

The Web has made possible many advanced text-mining applications, such as news summarization, essay grading, question answering, and semantic search. For many of such applications, statistical text-mining techniques are ineffective since they do not utilize the morphological structure of the text. Thus, many approaches use NLP-based techniques, that parse the text and use patterns to mine and analyze the parse trees which are often unnecessarily complex. Therefore, we propose a weighted-graph representation of text, called TextGraphs, which captures the grammatical and semantic relations between words and terms in the text. TextGraphs are generated using a new text mining framework which is the main focus of this paper. Our framework, SemScape, uses a statistical parser to generate few of the most probable parse trees for each sentence and employs a novel two-step pattern-based technique to extract from parse trees candidate terms and their grammatical relations. Moreover, SemScape resolves coreferences by a novel technique, generates domain-specific TextGraphs by consulting ontologies, and provides a SPARQL-like query language and an optimized engine for semantically querying and mining TextGraphs.

Ontologies are a vital component of most knowledge-based applications, including semantic web search, intelligent information integration, and natural language processing. In particular, we need effective tools for generating in-depth ontologies that achieve comprehensive converge of specific application domains of interest, while minimizing the time and cost of this process. Therefore we cannot rely on the manual or highly supervised approaches often used in the past, since they do not scale well. We instead propose a new approach that automatically generates domain-specific ontologies from a small corpus of documents using deep NLP-based text-mining. Starting from an initial small seed of domain concepts, our Onto Harvester system iteratively extracts ontological relations connecting existing concepts to other terms in the text, and adds strongly connected terms to the current ontology. As a result, Onto Harvester (i) remains focused on the application domain, (ii) is resistant to noise, and (iii) generates very comprehensive ontologies from modest-size document corpora. In fact, starting from a small seed, Onto Harvester produces ontologies that outperform both manually generated ontologies and ontologies generated by current techniques, even those that require very large well-focused data sets.

Computer-based instructional simulations are becoming more and more ubiquitous, particularly in military and medical domains. As the technology that drives these simulations grows ever more sophisticated, the underlying pedagogical models for how instruction, assessment, and feedback are implemented within these systems must evolve accordingly. In this article, we review some of the existing educational approaches to medical simulations, and present pedagogical methodologies that have been used in the design and development of games and simulations at the University of California, Los Angeles, Center for Research on Evaluation, Standards, and Student Testing. In particular, we present a methodology for how automated assessments of computer-based simulations can be implemented using ontologies and Bayesian networks, and discuss their advantages and design considerations for pedagogical use.

Ontologies are a vital component of most knowledge-based applications, such as semantic web search, intelligent information integration, and natural language processing. In the last two decades, many manual or highly supervised techniques were proposed to generate domain-specific ontologies. However, these approaches are usually very time-consuming and resource-demanding, and thus not scalable. To address this issue, numerous approaches have been recently proposed to automatically generate ontologies. These approaches mostly employ statistical or shallow NLP-based methods which usually limit the quality of their results. In this paper, we introduce OntoHarvester, a deep NLP-based system to automatically extract and populate domain-specific ontologies from morphological structures in the free text. Using our text mining framework, called SemScape, OntoHarvester converts text into graph structures called TextGraphs in which nodes represent candidate terms in the text and edges represent grammatical relations between the terms. Unlike most existing techniques that separate the concept extraction process from the semantic relation extraction, OntoHarvester integrates these two processes by starting with a small seed of domain’s concepts and iteratively performing the following two steps. i) Using graph-based patterns over the TextGraphs, it finds taxonomic relations between the concepts in the current ontology and other candidate terms. ii) From the candidate terms found in the previous step, OntoHarvester adds highly frequent and confident ones to the current ontology. OntoHarvester repeats the above two steps until the number of newly extracted concepts is smaller than a pre-defined threshold. This incremental process makes OntoHarvester more focused on the specified domain and more robust to noise. Our extended evaluation indicates that OntoHarvester improves the accuracy and coverage with respect to the existing methods, and starting from small initial seeds, it provides high-quality results for different domains.

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