Iris K. Howley

Exploring the Effect of Confusion in Discussion Forums of Massive Open Online Courses

Thousands of students enroll in Massive Open Online Courses~(MOOCs) to seek opportunities for learning and self-improvement. However, the learning process often involves struggles with confusion, which may have an adverse effect on the course participation experience, leading to dropout along the way. In this paper, we quantify that effect. We describe a classification model using discussion forum behavior and clickstream data to automatically identify posts that express confusion. We then apply survival analysis to quantify the impact of confusion on student dropout. The results demonstrate that the more confusion students express or are exposed to, the lower the probability of their retention. Receiving support and resolution of confusion helps mitigate this effect. We explore the differential effects of confusion expressed in different contexts and related to different aspects of courses. We conclude with implications for design of interventions towards improving the retention of students in MOOCs.

Can a Social Robot Stimulate Science Curiosity in Classrooms

This study investigates whether the presence of a social robot and interaction with it raises children’s interest in science. We placed Robovie, our social robot, in an elementary school science class where children could freely interact with it during their breaks. Robovie was tele-operated and its behaviors were designed to answer any questions related to science. It encouraged the children to ask about science by initiating conversations about class topics. Our result shows that even though Robovie did not influence the science curiosity of the entire class, there were individual increases in the children who asked Robovie science questions.

Towards academically productive talk supported by conversational agents

In this paper, we investigate the use of conversational agents to scaffold on-line collaborative learning discussions through an approach called academically productive talk. In contrast to past work, which has involved using agents to elevate the conceptual depth of collaborative discussion by leading students in groups through directed lines of reasoning, this approach lets students follow their own lines of reasoning and promotes productive practices such as explaining, stating agreement and disagreement, and reading and revoicing the statements of other students. We contrast two types of academically productive talk support for a discussion about 9th grade biology and show that one type in particular has a positive effect on the overall conversation, while the other is worse than no support. This positive effect carries over onto participation in a full-class discussion the following day. We use a sociolinguistic style analysis to investigate how the two types of support influence the discussion and draw conclusions for redesign. In particular, our findings have implications for how dynamic micro-scripting agents such as those scaffolding academically productive talk can be used in consort with more static macro- and micro- scripting.

Enhancing Scientific Reasoning and Discussion with Conversational Agents

This paper investigates the use of conversational agents to scaffold online collaborative learning discussions through an approach called academically productive talk (APT). In contrast to past work on dynamic support for collaborative learning, which has involved using agents to elevate the conceptual depth of collaborative discussion by leading students in groups through directed lines of reasoning, this APT-based approach lets students follow their own lines of reasoning and promotes productive practices such as explanation of reasoning and refinement of ideas. Two forms of support are contrasted, namely, Revoicing support and Feedback support. The study provides evidence that Revoicing support resulted in significantly more intensive reasoning exchange between students in the chat and significantly more learning during the chat than when that form of support was absent. Another form of support, namely, Feedback support increased expression of reasoning while marginally decreasing the intensity of the interaction between students and did not affect learning.

Effects of social presence and social role on help-seeking and learning

The unique social presence of robots can be leveraged in learning situations to reduce student evaluation anxiety, while still providing instructional guidance. Furthermore, social role of the instructor can also impact the prevalence of evaluation apprehension. In this study, we examine how human and robot social role affects help-seeking behaviors and learning outcomes in a one-on-one tutoring setting. Our results show that help-seeking is a moderator of the relationship between condition and learning, with the “human teacher” condition resulting in significantly less learning (and marginally less help-seeking) than the “human assistant” and both robot conditions. Categories and Subject Descriptors H.5.2 [Information Interfaces and Presentation]: User Interfaces-Interaction styles. K.3.1 [Computer Uses in Education]: Computer-assisted Instruction. General Terms Experimentation, Human Factors.

Gaining Insights from Sociolinguistic Style Analysis for Redesign of Conversational Agent Based Support for Collaborative Learning

Data from an early stage of development of conversational agent based support for collaborative learning provides an ideal resource for demonstrating the value of sociolinguistic style analysis paired with time series visualizations as part of an iterative design process. The methodology illustrated in this chapter was introduced in earlier publications focusing separately on the sociolinguistic style analysis (Howley and Rose, Modeling the rhetoric of human-computer interaction. In: HCII’11 Proceedings of the 14th international conference on Human-computer interaction: interaction techniques and environments, pp 341–350, Springer-Verlag, Berlin, Heidelberg, 2011; Howley et al., A multivocal process analysis of social positioning in study groups. In: Suthers et al. (eds). Productive multivocality in the analysis of group interactions, Springer, 2013) and the time series visualization using the Tatiana tool (Dyke et al., Challenging assumptions: using sliding window visualizations to reveal time-based irregularities in CSCL processes. In: Proceedings of the international conference of the learning sciences. Sydney, Australia, 2012). However this chapter is unique in its application to data that is at such an early stage in a development process. The data is admittedly raw, and contains many examples of interaction gone awry. Nevertheless, the value in this analysis is in a demonstration of what insights can be gained through detailed stylistic analysis of conversational behavior that informs the next steps of intervention development.

A Multivocal Process Analysis of Social Positioning in Study Groups

This chapter compares two multidimensional analyses of the PLTL Chemistry dataset, which each include a cognitive, relational, and motivational dimension. These multidimensional analyses serve to highlight the ways in which the complementary perspectives on collaborative processes offered by each dimension can be integrated in a way that offers deep insights into social positioning within collaborative groups. Differences revealed particularly along the relational and motivational dimensions raise important questions regarding the operationalization of interaction style as displayed through language and highlight the value of multivocality for the purpose of refining important constructs in ways that work towards theory building through integration of findings across research groups that employ different analytic frameworks coming from a common theoretical foundation.

Group composition and intelligent dialogue tutors for impacting students' academic self-efficacy

In this paper, we explore using an intelligent dialogue tutor to influence student academic self-efficacy, as well as its interaction with group self-efficacy composition in a dyadic learning environment. We find providing additional tutor prompts encouraging students to participate in discussion may have unexpected negative effects on self-efficacy, especially on students with low self-efficacy scores who have partners with low self-efficacy scores.

Motivation and collaborative behavior: an exploratory analysis

The motivating effects of collaborative learning have long been argued, however a careful analysis of the relationship between the motivation orientation of a student and perceptions of himself, his partners, his collaborative behaviors, and learning in a collaborative context have not been as thoroughly explored. In this paper we present an exploratory analysis of data from a collaborative learning study from the standpoint of motivation type of students and their partners. Overall, what we see is that a students own motivation orientation may color their perception of the exchange of help in the collaboration, sometimes obscuring the reality of the help actually exchanged.

Alleviating the Negative Effect of Up and Downvoting on Help Seeking in MOOC Discussion Forums

Through the lens of Expectancy Value Theory, we examine the effect of help giver badges, information about helper expertise, and up- and downvoting on help seeking in a MOOC discussion forum. Results show that badges alleviated the negative impact on help seeking introduced by up- and downvoting.