The relationship of (perceived) epistemic cognition to interaction with resources on the internet

Information seeking and processing are key literacy practices. However, they are activities that students, across a range of ages, struggle with. These information seeking processes can be viewed through the lens of epistemic cognition: beliefs regarding the source, justification, complexity, and certainty of knowledge. In the research reported in this article we build on established research in this area, which has typically used self-report psychometric and behavior data, and information seeking tasks involving closed-document sets. We take a novel approach in applying established self-report measures to a large-scale, naturalistic, study environment, pointing to the potential of analysis of dialogue, web-navigation – including sites visited – and other trace data, to support more traditional self-report mechanisms. Our analysis suggests that prior work demonstrating relationships between self-report indicators is not paralleled in investigation of the hypothesized relationships between self-report and trace-indicators. However, there are clear epistemic features of this trace data. The article thus demonstrates the potential of behavioral learning analytic data in understanding how epistemic cognition is brought to bear in rich information seeking and processing tasks

Hybrid DOM-Sensitive Change Impact Analysis for JavaScript

JavaScript has grown to be among the most popular programming languages. However, performing change impact analysis on JavaScript applications is challenging due to features such as the seamless interplay with the DOM, event-driven and dynamic function calls, and asynchronous client/server communication. We first perform an empirical study of change propagation, the results of which show that the DOM-related and dynamic features of JavaScript need to be taken into consideration in the analysis since they affect change impact propagation. We propose a DOM-sensitive hybrid change impact analysis technique for Javascript through a combination of static and dynamic analysis. The proposed approach incorporates a novel ranking algorithm for indicating the importance of each entity in the impact set. Our approach is implemented in a tool called Tochal. The results of our evaluation reveal that Tochal provides a more complete analysis compared to static or dynamic methods. Moreover, through an industrial controlled experiment, we find that Tochal helps developers by improving their task completion duration by 78% and accuracy by 223%

Student Teachers’ Experiences with a Preparation-to-Practice Gap in Reading Instruction: A Preliminary Exploration and Implications for Teacher Preparation Faculty

Abstract Teacher educators and practitioners can agree that there are differences between knowing something in theory and knowing how to do something in a real classroom. This qualitative inquiry is anchored in evidence-based reading instruction as described by the National Reading Panel (2000) which emphasizes systematic, explicit instructional and teaching enhancements to support diverse students’ learning in multi-tier general educational classrooms. Specifically, this study investigated how student teachers applied their knowledge of research based reading methods in general education classrooms during their capstone field experience at the end of their undergraduate program, hereafter called student teaching

An empirical study on code comprehension: DCI compared to OO

Comprehension of source code affects software development, especially its maintenance where reading code is the most time consuming performed activity. A programming paradigm imposes a style of arranging the source code that is aligned with a way of thinking toward a computable solution. Then, a programming paradigm with a programming language represents an important factor for source code comprehension. Object-Oriented (OO) is the dominant paradigm today. Although, it was criticized from its beginning and recently an alternative has been proposed. In an OO source code, system functions cannot escape outside the definition of classes and their descriptions live inside multiple class declarations. This results in an obfuscated code, a lost sense the run-time, and in a lack of global knowledge that weaken the understandability of the source code at system level. A new paradigm is emerging to address these and other OO issues, this is the Data Context Interaction (DCI) paradigm. We conducted the first human subject related controlled experiment to evaluate the effects of DCI on code comprehension compared to OO. We looked for correctness, time consumption, and focus of attention during comprehension tasks. We also present a novel approach using metrics from Social Network Analysis to analyze what we call the Cognitive Network of Language Elements (CNLE) that is built by programmers while comprehending a system. We consider this approach useful to understand source code properties uncovered from code reading cognitive tasks. The results obtained are preliminary in nature but indicate that DCI-trygve approach produces more comprehensible source code and promotes a stronger focus the attention in important files when programmers are reading code during program comprehension. Regarding reading time spent on files, we were not able to indicate with statistical significance which approach allows programmers to consume less time

Understanding Eye Gaze Patterns in Code Comprehension

Program comprehension is a sub-field of software engineering that seeks to understand how developers understand programs. Comprehension acts as a starting point for many software engineering tasks such as bug fixing, refactoring, and feature creation. The dissertation presents a series of empirical studies to understand how developers comprehend software in realistic settings. The unique aspect of this work is the use of eye tracking equipment to gather fine-grained detailed information of what developers look at in software artifacts while they perform realistic tasks in an environment familiar to them, namely a context including both the Integrated Development Environment (Eclipse or Visual Studio) and a web browser (Google Chrome). The iTrace eye tracking infrastructure is used for certain eye tracking studies on large code files as it is able to handle page scrolling and context switching. The first study is a classroom-based study on how students actively trained in the classroom understand grouped units of C++ code. Results indicate students made many transitions between lines that were closer together, and were attracted the most to if statements and to a lesser extent assignment code. The second study seeks to understand how developers use Stack Overflow page elements to build summaries of open source project code. Results indicate participants focused more heavily on question and answer text, and the embedded code, more than they did the title, question tags, or votes. The third study presents a larger code summarization study using different information contexts: Stack Overflow, bug repositories and source code. Results show participants tended to visit up to two codebase files in either the combined or isolated codebase session, but visit more bug report pages, and spend longer time on new Stack Overflow pages they visited, when given either these two treatments in isolation. In the combined session, time spent on the one or two codebase files they viewed dominated the session time. Information learned from tracking developers\u27 gaze in these studies can form foundations for developer behavior models, which we hope can later inform recommendations for actions one might take to achieve workflow goals in these settings. Advisor: Bonita Shari

Speech-language pathologists\u27 input to toddlers in early intervention: a pilot study

Caregivers interacting with young children in natural settings have been found to provide language input that is in tune with the child\u27s output in terms of mean length of utterance (MLU). Previous research suggests that caregivers provide language input within the child\u27s proximal zone of language development, that is 2.0-3.0 morphemes ahead of their child\u27s MLU. The purpose of this exploratory study was to investigate whether speech-language pathologists (SLP) working in early intervention tailor their input in the same way. Communication interactions between six speech-language pathologists and their toddler aged clients between the ages of 28 and 33 months were audio recorded during one of their regularly scheduled speech and language intervention sessions. MLUs for the SLPs and the children were calculated for each intervention dyad via the Systematic Analysis of Language Transcripts (SALT) version 2012 computer software program. The MLU of each SLP was then compared to the MLU of her client. Data analysis revealed that three of the six SLPs directed their language input to the child at levels within the child\u27s proximal zone of language development, between 2.0 and 3.0 morphemes greater than the child\u27s MLU. The other three SLPs provided input at levels that exceeded the 2.0 to 3.0 morpheme range. Qualitative analysis suggest that factors other than the children\u27s MLUs, such as their language comprehension levels, may have been a factor in the complexity levels of the SLPs input. Future research, employing larger sample sizes and careful measures of the children\u27s language comprehension and cognitive levels, is indicated