18,848 research outputs found

    A Computational Model of Children's Semantic Memory

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    A computational model of children's semantic memory is built from the Latent Semantic Analysis (LSA) of a multisource child corpus. Three tests of the model are described, simulating a vocabulary test, an association test and a recall task. For each one, results from experiments with children are presented and compared to the model data. Adequacy is correct, which means that this simulation of children's semantic memory can be used to simulate a variety of children's cognitive processes

    Judith Butler's Critique of Binary Gender Opposition in Gender Trouble: A Task-Based Lesson Sequence

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    This chapter presents a task-based lesson sequence based on Judith Butler's Gender Trouble. Gender Trouble is a great piece of philosophical literature. However, as philosophical literature is a genre rarely found in EFL teaching, this chapter first demonstrates in detail the merits of this genre for the teaching ofEnglish for Academic Purposes. After a brief analysis of the source text, which deconstructs the entire sex-gender link and presents both sex and gender as free-floating, this chapter presents task-based methodology and how it is utilized in a lesson aimed at building gender awareness and acceptance. In the target task students are asked to take the role of an ethics teacher at an Irish high school, in which the discussion arose whether the school should introduce unisex toilets and changing rooms in order to not discriminate against transsexual students. Tue study of Butler's philosophy will provide students with both the knowledge and language to accomplish this task. Open follow-up discussions often lead to powerful ethical insights in the context of gender, homo- and transsexuality

    The effects of change decomposition on code review -- a controlled experiment

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    Background: Code review is a cognitively demanding and time-consuming process. Previous qualitative studies hinted at how decomposing change sets into multiple yet internally coherent ones would improve the reviewing process. So far, literature provided no quantitative analysis of this hypothesis. Aims: (1) Quantitatively measure the effects of change decomposition on the outcome of code review (in terms of number of found defects, wrongly reported issues, suggested improvements, time, and understanding); (2) Qualitatively analyze how subjects approach the review and navigate the code, building knowledge and addressing existing issues, in large vs. decomposed changes. Method: Controlled experiment using the pull-based development model involving 28 software developers among professionals and graduate students. Results: Change decomposition leads to fewer wrongly reported issues, influences how subjects approach and conduct the review activity (by increasing context-seeking), yet impacts neither understanding the change rationale nor the number of found defects. Conclusions: Change decomposition reduces the noise for subsequent data analyses but also significantly supports the tasks of the developers in charge of reviewing the changes. As such, commits belonging to different concepts should be separated, adopting this as a best practice in software engineering

    Developing and Validating Stealth Assessments for an Educational Game to Assess Young Dual Language Immersion Learners\u27 Reading Comprehension

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    The purpose of this multiple-paper dissertation is to design a digital game and stealth assessments within the game to assess young second language learners\u27 Chinese reading proficiency. In Chapter 2 (Paper 1), I describe the game designed for this dissertation and how it was implemented in a dual language immersion classroom. This study found that the digital game and in-class implementation led to significant vocabulary and reading comprehension gains. Further, seven types of support that students needed while playing the game were identified. In Chapter 3 (Paper 2), I describe how educational data mining approaches, and more specifically, how data-driven explorations, can provide insight into how players interact with the game and further how those interactions relate to proficiency and learning. In this study, I identify time on task and use of an in-game glossing tool as important indicators for learning. In addition, four subgroups of students were identified based on their gameplay styles. Finally, in Chapter 4 (Paper 3), I describe how stealth assessments were designed and validated within the game. This study found that the stealth assessments were significantly correlated with two external measures of reading comprehension

    Applying educational data mining to explore individual experiences in digital games

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    Research involving digital games and language learning is rapidly growing. One advantage of using digital games to support language learning is the ability to collect data on students learning in real time. In this study, we use educational data mining methods to explore the relationship between in-game data and elementary students’ Chinese language learning. Thirty-six students in the sixth grade played a digital game for eight 25-minute sessions as part of their Chinese Dual Language Immersion classroom instruction. We used classification and regression tree analyses and cluster analyses to explore how in- game indicators, such as battles, time spent reading a text, and the use of an in-game glossing tool are associated with language learning and change in affect. The results indicate that time on task and use of the glossing tool were the most important variables in determining language learning gains. We also identified four subgroups of gameplay styles. While there were no significant differences in learning or affective factors based on the subgroups, these gameplay styles allow for a more individualized approach to analyzing learning within digital environment

    Links between the personalities, styles and performance in computer programming

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    There are repetitive patterns in strategies of manipulating source code. For example, modifying source code before acquiring knowledge of how a code works is a depth-first style and reading and understanding before modifying source code is a breadth-first style. To the extent we know there is no study on the influence of personality on them. The objective of this study is to understand the influence of personality on programming styles. We did a correlational study with 65 programmers at the University of Stuttgart. Academic achievement, programming experience, attitude towards programming and five personality factors were measured via self-assessed survey. The programming styles were asked in the survey or mined from the software repositories. Performance in programming was composed of bug-proneness of programmers which was mined from software repositories, the grades they got in a software project course and their estimate of their own programming ability. We did statistical analysis and found that Openness to Experience has a positive association with breadth-first style and Conscientiousness has a positive association with depth-first style. We also found that in addition to having more programming experience and better academic achievement, the styles of working depth-first and saving coarse-grained revisions improve performance in programming.Comment: 27 pages, 6 figure

    Analytics of Open-Book Exams with Interaction Traces in a Humanities Course

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    [29TH INTERNATIONAL CONFERENCE ON COMPUTERS IN EDUCATION] 22-26 November 2021, CyberspaceOpen book exams (OBE) have been a mandated part of each course structure at some universities. Also during the COVID19 emergency remote teaching situation, OBE would be an option for many instructors over a proctored examination. In this study we investigate a Critical Analysis of Literature and Cinema course which had offered open book exam components for more than 11 years in a face-to-face classroom mode. However, this time the OBE was conducted online using BookRoll, a learning analytics enhanced eBook platform. 89 Students accessed Hayavadana, an Indian play uploaded on BookRoll during the exam. They attempted a critical reading task to identify performative elements and cultural references in the text by highlighting them with yellow and red markers respectively and writing a reflective memo about the identified items in BookRoll. We analyzed learner’s interaction logs gathered in the learning record store linked to BookRoll during the OBE and investigated the relations between their critical reading behaviors to the OBE achievement. Further, selecting two distinct achievement groups we conducted process mining to identify distinct reading behaviors of the high and low performers and give examples of their generated reflective memos. This study aims to initiate further discussion related to the application of learning analytics in humanities courses and probed into the behaviors of the students during the OBE
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