Gahgene Gweon

Hooked on smartphones: an exploratory study on smartphone overuse among college students

The negative aspects of smartphone overuse on young adults, such as sleep deprivation and attention deficits, are being increasingly recognized recently. This emerging issue motivated us to analyze the usage patterns related to smartphone overuse. We investigate smartphone usage for 95 college students using surveys, logged data, and interviews. We first divide the participants into risk and non-risk groups based on self-reported rating scale for smartphone overuse. We then analyze the usage data to identify between-group usage differences, which ranged from the overall usage patterns to app-specific usage patterns. Compared with the non-risk group, our results show that the risk group has longer usage time per day and different diurnal usage patterns. Also, the risk group users are more susceptible to push notifications, and tend to consume more online content. We characterize the overall relationship between usage features and smartphone overuse using analytic modeling and provide detailed illustrations of problematic usage behaviors based on interview data.

Providing support for adaptive scripting in an on-line collaborative learning environment

This paper describes results from a series of experimental studies to explore issues related to structuring productive group dynamics for collaborative learning using an adaptive support mechanism. The first study provides evidence in favor of the feasibility of the endeavor by demonstrating with a tightly controlled study that even without adaptive support, problem solving in pairs is significantly more effective for learning than problem solving alone. The results from a second study offer guidelines for strategic matching of students with learning partners. Furthermore, the results reveal specific areas for needed support. Based on the results from the second study, we present the design of an adaptive support mechanism, which we evaluate in a third study. The results from the third study provide evidence that certain aspects of our design for adaptive support in the form of strategic prompts are effective for manipulating student behavior in productive ways and for supporting learning. These results also motivate specific modifications to the original design.

Measuring prevalence of other-oriented transactive contributions using an automated measure of speech style accommodation

This paper contributes to a theory-grounded methodological foundation for automatic collaborative learning process analysis. It does this by illustrating how insights from the social psychology and sociolinguistics of speech style provide a theoretical framework to inform the design of a computational model. The purpose of that model is to detect prevalence of an important group knowledge integration process in raw speech data. Specifically, this paper focuses on assessment of transactivity in dyadic discussions, where a transactive contribution is operationalized as one where reasoning is made explicit, and where that reasoning builds on a prior reasoning statement within the discussion. Transactive contributions can be either self-oriented, where the contribution builds on the speaker’s own prior contribution, or other-oriented, where the contribution builds on a prior contribution of a conversational partner. Other-oriented transacts are particularly central to group knowledge integration processes. An unsupervised Dynamic Bayesian Network model motivated by concepts from Speech Accommodation Theory is presented and then evaluated on the task of estimating prevalence of other-oriented transacts in dyadic discussions. The evaluation demonstrates a significant positive correlation between an automatic measure of speech style accommodation and prevalence of other-oriented transacts (Ru2009=u2009.36, pu2009<u2009.05).

A Framework for Assessment of Student Project Groups On-Line and Off-Line

Assessment of difficulties within group processes, especially through automatic means, is a problem of great interest to the broader CSCL community. Group difficulties can be revealed through interaction processes that occur during group work. Whether these patterns are encoded in speech recorded from face-to-face interactions or in text from on-line interactions, the language communication that flows between group members is an important key to understanding how better to support group functions and therefore be in a better position to design effective group learning environments. With the capability of monitoring and then influencing group processes when problems are detected, it is possible to intervene in order to facilitate the accomplishment of a higher quality product. In this chapter we address this research problem of monitoring group work processes in a context where project course instructors are making assessments of student group work. Thus, our purpose is to support those instructors in their task. We describe the mixed methods approach that we took, which combines both an interview study and a classroom study. Three research questions are answered: (1) What do instructors want to know about their student groups? (2) Is the desired information observable, and can it be reliably tracked by human annotators? (3) Can the desired information be automatically tracked using machine learning techniques to produce a summary report that instructors can use? Based on interviews with nine instructors, we identified five process assessment categories with subcategories at the group and individual level: namely, goal setting, group and individual progress, knowledge contribution, participation, and teamwork. We verified that these assessment categories can be reliably coded during group meetings with a reliability of r = 0.80 at the group level and r = 0.64 at the individual level using carefully constructed human assessment instruments. We present work in progress towards automation of this assessment framework.

Evaluating the effect of feedback from a CSCL problem solving environment on learning, interaction, and perceived interdependence

In this paper, we explore the effect of the form of feedback offered by a computer supported collaborative learning (CSCL) environment on the roles that students see themselves as taking and that their behavior reflects. We do this by experimentally contrasting collaboration in two feedback configurations, one which is identical to the state-of-the-art in intelligent tutoring technology (Immediate Feedback), and one which is based on a long line of investigation of the use of worked out examples for instruction (Delayed Feedback). While our conclusions remain tentative due to the small sample size, the data reveal a consistent gender by condition interaction pattern across questionnaire, test, and discourse data in which male students prefer and benefit more from collaboration in the Immediate Feedback condition where they are more likely to take on the role of a help provider rather than a help receiver while the patterns is the opposite for females.

Helping Agents in VMT

In this chapter we describe ongoing work towards enabling dynamic support for collaborative learning in the Virtual Math Teams (VMT) environment using state-of-the-art language technologies such as text classification and dialogue agents. The key research goal of our long-term partnership is to experimentally learn broadly applicable principles for supporting effective collaborative problem solving by using these technologies to elicit behavior such as reflection, help seeking, and help provision, which are productive for student learning in diverse groups. Our work so far has yielded an integrated system that makes technology for dynamic collaborative learning support—which has proved effective in earlier lab and classroom studies—available for experimental use within the “wild” VMT environment.

Evaluating an Automated Tool to Assist Evolutionary Document Generation

While using how-to documents for guidance in performing computer-based tasks, users often run into problems due to inaccurate, out-of-date and incomplete documentation. These problems are often due to current documentation practices, which fail to keep how-to documents current, accurate, and complete. We believe that automated support for incremental update of how-to-documents, through the use of programming by demonstration and guided walkthrough techniques, is more effective than existing practice and produces documents that cause fewer problems for their users. In this paper, we present a study that evaluates this belief by comparing DocWizards, a tool utilizing these techniques, with a standard word processor. We show that more effective and efficient documentation can be generated by multiple authors using DocWizards in an incremental process, with effort comparable to that incurred using a traditional tool.

TOWARDS AN INTERACTIVE ASSESSMENT FRAMEWORK FOR ENGINEERING DESIGN LEARNING

In this paper we explore the use of text processing technology for on-line assessment in an engineering design project class. We present results from a 5-week classroom study in a capstone engineering design course in which we explore the potential benefits of such technology for student learning in this context. Furthermore, we present results from ongoing work assessing student productivity based on features extracted from their conversational behavior in the course discussion board. While we found that typical shallow productivity measures such as number of posts, length of posts, or number of files committed have no correlation with an instructor assigned grade, we can achieve a substantial improvement using simple linguistic patterns extracted from on-line conversational behavior.© 2007 ASME

Exposing middle school girls to programming via creative tools

This paper explores design concepts and principles to engage middle school girls in learning preliminary programming concepts through different media and interaction techniques. Creating a greeting card and creating a personal avatar for an Instant Messenger (IM) were two approaches that were examined. Findings suggest that an IM avatar creation tool, with guiding principles including partial manipulation of code, immediate feedback, engaging content, reinforcement exercises, and transition from concrete to abstract examples, may interest girls to start learning programming concepts.

Supporting efficient and reliable content analysis using automatic text processing technology

Text categorization technology can be used to streamline the process of content analysis of corpus data. However, while recent results for automatic corpus analysis show great promise, tools that are currently being used for HCI research and practice do not make use of it. Here, we empirically evaluate trade-offs between semi automatic and hand labeling of data in terms of speed, validity, and reliability of coding in order to assess the usefulness of incorporating this technology into HCI tools.