Argument Component Classification for Classroom Discussions

This paper focuses on argument component classification for transcribed spoken classroom discussions, with the goal of automatically classifying student utterances into claims, evidence, and warrants. We show that an existing method for argument component classification developed for another educationally-oriented domain performs poorly on our dataset. We then show that feature sets from prior work on argument mining for student essays and online dialogues can be used to improve performance considerably. We also provide a comparison between convolutional neural networks and recurrent neural networks when trained under different conditions to classify argument components in classroom discussions. While neural network models are not always able to outperform a logistic regression model, we were able to gain some useful insights: convolutional networks are more robust than recurrent networks both at the character and at the word level, and specificity information can help boost performance in multi-task training

A return to Teacherbot:Rethinking the development of educational technology at the University of Edinburgh

In the market discourses of technological disruption, higher education institutions have routinely been positioned in deficit models and of anachronistic approaches to teaching at odds with the types of educational futures being presented by commercial organisations. Predominantly, automation technologies in the form of artificial intelligence are being promoted as the future of teaching. In this paper, on the other hand, we explore the prospects for using non-artificial intelligence automated agents in teaching and its impact on the teacher function at the University of Edinburgh. Through engagement with teachers, staff and students at the university, this research has identified use cases for bots, in what spaces they would be situated, and how they would supplement the teacher function. This paper argues that a community-driven approach combined with a sociomaterial conceptualisation can generate a shift from market discourses and to collaborative development of educational technologies

Enhance Classroom Preparation for Flipped Classroom using AI and Analytics

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Will Artificial Intelligence Enable Open Universities to Regain their Past Glory in the 21st Century?

Leveraging technology to break the iron triangle of access, cost, and quality is a legacy of open universities (OUs), becoming a source of inspiration for higher education in general. Today, OUs face increasing competition from conventional universities, no longer enjoying the first-mover advantages as they did in the earlier years. Can artificial intelligence (AI) enable OUs to stay competitive in the 21st century as other technologies did in the past? This paper first reviews literature on the affordances or (potential) uses of AI for open and distance education and then examines the implications of these affordances for OUs in terms of quality, cost, and access. It concludes by arguing for a systems approach to exploring how OUs can remain open as to people and places as well as to methods and ideas by making creative and innovative uses of AI

Supporting students in the analysis of case studies for professional ethics education

Intelligent tutoring systems and computer-supported collaborative environments have been designed to enhance human learning in various domains. While a number of solid techniques have been developed in the Artificial Intelligence in Education (AIED) field to foster human learning in fundamental science domains, there is still a lack of evidence about how to support learning in so-called ill-defined domains that are characterized by the absence of formal domain theories, uncertainty about best solution strategies and teaching practices, and learners' answers represented through text and argumentation. This dissertation investigates how to support students' learning in the ill-defined domain of professional ethics through a computer-based learning system. More specifically, it examines how to support students in the analysis of case studies, which is a common pedagogical practice in the ethics domain. This dissertation describes our design considerations and a resulting system called Umka. In Umka learners analyze case studies individually and collaboratively that pose some ethical or professional dilemmas. Umka provides various types of support to learners in the analysis task. In the individual analysis it provides various kinds of feedback to arguments of learners based on predefined system knowledge. In the collaborative analysis Umka fosters learners' interactions and self-reflection through system suggestions and a specifically designed visualization. The system suggestions offer learners the chance to consider certain helpful arguments of their peers, or to interact with certain helpful peers. The visualization highlights similarities and differences between the learners' positions, and illustrates the learners' level of acceptance of each other's positions. This dissertation reports on a series of experiments in which we evaluated the effectiveness of Umka's support features, and suggests several research contributions. Through this work, it is shown that despite the ill-definedness of the ethics domain, and the consequent complications of text processing and domain modelling, it is possible to build effective tutoring systems for supporting students' learning in this domain. Moreover, the techniques developed through this research for the ethics domain can be readily expanded to other ill-defined domains, where argument, qualitative analysis, metacognition and interaction over case studies are key pedagogical practices

COGNITIVE PRESENCE IN PEER FACILITATED ASYNCHRONOUS ONLINE DISCUSSION: THE PATTERNS AND HOW TO FACILITATE

This study, in the context of peer-facilitated asynchronous online discussion, explored the characteristics and patterns of students’ cognitive presence, and examined the practices that aim to enhance cognitive presence development. Participants were 53 students from a graduate-level online course that focused on the integration of educational technologies. Data were collected from discussion transcripts, student survey, student artifacts, and researcher’s observations. Results demonstrated four phases of students’ cognitive presence: Triggering event, Exploration, Integration, and Resolution. Among the four phases, students’ cognitive presence tended to aggregate at the middle phases: Integration and Exploration. Percentage of the Resolution was very low. The distribution of students’ discussion behaviors further revealed: a) the hierarchical relationship between the four phases: Integration and Resolution involved a higher-level of cognitive engagement, and Triggering event and Exploration involved a lower-level of cognitive engagement; b) the phase of Resolution heavily relied on experiment, while the other three phases heavily relied on making use of personal experience; c) creating of cognitive presence occurred in both the private space of individual activities and the shared space of having dialogues. The conversation analysis of threads and episodes explored the temporal evolvement of cognitive presence. The results showed that, in an ongoing discussion, students’ cognitive presence evolved in a non-linear way, rather than strictly phase by phase as suggested by the PI model. Experiments were designed and conducted to determine the effects of two pedagogical interventions – 1) providing guidance on peer facilitation techniques; 2) asking students to label their posts. The results showed that the Intervention 1 and the combination of two interventions credibly improved students’ cognitive presence. They were especially effective in improving Integration, a higher level of cognitive presence. After having added Intervention 2, cognitive presence increased from the first-half to the second-half semester, although the improvement was not found to be statistically credible. This study confirmed the close association between and among cognitive presence, social interaction, and peer facilitation. The results clearly showed that Intervention 1 – providing guidance on peer facilitation credibly improved students’ social interaction and peer facilitation. However, Mixed findings were obtained for Intervention 2 – asking students to label their posts. It was found that Intervention 2 positively increased students’ social interaction. However, it did not show any impact on students’ peer facilitation behaviors. It is also worth noting that the effect of the combination of two interventions was much larger than any single one of them. Conversation analysis was conducted to zoom in on the dynamic process of discussion. The cases revealed that when students were provided with the guidance on peer facilitation techniques, they tended to use a variety of facilitation techniques in a strategic way to help peers to achieve a sustained and deeper-level conversation. Compared to the control group, the students in the treatment group showed more peer facilitation behaviors, which led to more conversations and more higher-level cognitive presence. This study has unpacked the complexity of students’ cognitive presence in a peer-facilitated discussion environment, especially when students are coached in performing teaching presence. The results shed light on the pedagogical practices and strategies of creating an online learning community that incubates rich cognitive presence. Finally, implications are discussed for the research and practices in online instruction and discussion analytics