Section introduction: Dialogic education and digital technology

The chapters in this section of the book focus specifically on dialogic education and digital technology. To frame this chapter, it is important to understand why there should be mutual interest among those who are interested in the role of dialogic approaches, and the role of digital technologies in learning. At weakest such shared theorising is important simply because technology is increasingly available (indeed, pervasive) in our everyday lives and classrooms. In this view, technologies are more or less neutral actors to be leveraged as we wish; we should thus understand how to develop dialogic approaches in this emerging context. However, while of course rapid technological change creates an imperative to understand the impact of that change, this narrow perspective is a view that sociocultural researchers and those interested in dialogic approaches would reject. A somewhat stronger claim, then, and one that is made explicitly by Major and Warwick (this section) is that those who are interested in dialogic approaches to learning should be interested in digital technologies with respect to the affordances or possibilities for action that those technologies create for dialogue. A corollary, then, is that those interested in digital technologies should be interested in how they might develop and research tools that create or embody such affordances for dialogue and learning. Within this context, digital tools can be seen as affording opportunity to, for example, make learning visible to students and teachers as an artefact for reflection and improvement, creating sharing space to scrutinise ideas, and showing how ideas evolve over time. Moreover, as Major and Warwick note, we care not only about the action possibilities, but also the enacted affordances for dialogue – i.e., the specific ways in which the action possibilities are implicated in promotion of dialogic interaction for learning, and indeed, as Rasmussen et al note, the ways that new tools provide both new affordances (or possibilities) and obstacles. However, a stronger claim again is that we should be interested in the relationships between dialogic approaches to learning, and digital technologies for learning, because dialogue is both shaped by digital technologies, and helps to shape both the use and emergence of those technologies. That is, to use the language of Major and Warwick, in addition to technology creating affordances for dialogue, dialogue also creates affordances for particular uses of technology; the two are thus in mutually constitutive interaction. Put another way, Kumpulainen, Rajala, and Kajamaa (this section) distinguish material-dialogic spaces in which the focus is (1) about artefacts of digital technologies – i.e., dialogue centred on digital technology; (2) around digital technologies – i.e., dialogue that is in the context of these technologies, a context which is expanded by the very use of those digital technologies, through their affordances for dialogue; and (3) with or through digital technologies, which might be characterised in terms of meaning that is mutually constituted in and through the dialogue and materiality of the digital technologies. Each of these perspectives can be seen in the chapters in this section of the handbook, each with important implications for how we understand and foster dialogue approaches, and digital technologies, for learning

Supportive technologies for group discussion in MOOCs

A key hurdle that prevents MOOCs from reaching their transformative potential in terms of making valuable learning experiences available to the masses is providing support for students to make use of the resources they can provide for each other. This paper lays the foundation for meeting this challenge by beginning with a case study and computational modeling of social interaction data. The analysis yields new knowledge that informs design and development of novel, real-time support for building healthy learning communities that foster a high level of engagement and learning. We conclude by suggesting specific areas for potential impact of new technology

Learnersourcing Personalized Hints

Personalized support for students is a gold standard in education, but it scales poorly with the number of students. Prior work on learnersourcing presented an approach for learners to engage in human computation tasks while trying to learn a new skill. Our key insight is that students, through their own experience struggling with a particular problem, can become experts on the particular optimizations they implement or bugs they resolve. These students can then generate hints for fellow students based on their new expertise. We present workflows that harvest and organize studentsâ collective knowledge and advice for helping fellow novices through design problems in engineering. Systems embodying each workflow were evaluated in the context of a college-level computer architecture class with an enrollment of more than two hundred students each semester. We show that, given our design choices, students can create helpful hints for their peers that augment or even replace teachersâ personalized assistance, when that assistance is not available

Analyzing collaborative learning processes automatically

In this article we describe the emerging area of text classification research focused on the problem of collaborative learning process analysis both from a broad perspective and more specifically in terms of a publicly available tool set called TagHelper tools. Analyzing the variety of pedagogically valuable facets of learners’ interactions is a time consuming and effortful process. Improving automated analyses of such highly valued processes of collaborative learning by adapting and applying recent text classification technologies would make it a less arduous task to obtain insights from corpus data. This endeavor also holds the potential for enabling substantially improved on-line instruction both by providing teachers and facilitators with reports about the groups they are moderating and by triggering context sensitive collaborative learning support on an as-needed basis. In this article, we report on an interdisciplinary research project, which has been investigating the effectiveness of applying text classification technology to a large CSCL corpus that has been analyzed by human coders using a theory-based multidimensional coding scheme. We report promising results and include an in-depth discussion of important issues such as reliability, validity, and efficiency that should be considered when deciding on the appropriateness of adopting a new technology such as TagHelper tools. One major technical contribution of this work is a demonstration that an important piece of the work towards making text classification technology effective for this purpose is designing and building linguistic pattern detectors, otherwise known as features, that can be extracted reliably from texts and that have high predictive power for the categories of discourse actions that the CSCL community is interested in

Can Natural Language Processing Become Natural Language Coaching?

How we teach and learn is undergoing a revolution, due to changes in technology and connectivity. Education may be one of the best application areas for advanced NLP techniques, and NLP researchers have much to contribute to this problem, especially in the areas of learning to write, mastery learning, and peer learning. In this paper I consider what happens when we convert natural language processors into natural language coaches. 1 Why Should You Care, NLP Researcher? There is a revolution in learning underway. Stu

Designing Adaptive Instruction for Teams: a Meta-Analysis

The goal of this research was the development of a practical architecture for the computer-based tutoring of teams. This article examines the relationship of team behaviors as antecedents to successful team performance and learning during adaptive instruction guided by Intelligent Tutoring Systems (ITSs). Adaptive instruction is a training or educational experience tailored by artificially-intelligent, computer-based tutors with the goal of optimizing learner outcomes (e.g., knowledge and skill acquisition, performance, enhanced retention, accelerated learning, or transfer of skills from instructional environments to work environments). The core contribution of this research was the identification of behavioral markers associated with the antecedents of team performance and learning thus enabling the development and refinement of teamwork models in ITS architectures. Teamwork focuses on the coordination, cooperation, and communication among individuals to achieve a shared goal. For ITSs to optimally tailor team instruction, tutors must have key insights about both the team and the learners on that team. To aid the modeling of teams, we examined the literature to evaluate the relationship of teamwork behaviors (e.g., communication, cooperation, coordination, cognition, leadership/coaching, and conflict) with team outcomes (learning, performance, satisfaction, and viability) as part of a large-scale meta-analysis of the ITS, team training, and team performance literature. While ITSs have been used infrequently to instruct teams, the goal of this meta-analysis make team tutoring more ubiquitous by: identifying significant relationships between team behaviors and effective performance and learning outcomes; developing instructional guidelines for team tutoring based on these relationships; and applying these team tutoring guidelines to the Generalized Intelligent Framework for Tutoring (GIFT), an open source architecture for authoring, delivering, managing, and evaluating adaptive instructional tools and methods. In doing this, we have designed a domain-independent framework for the adaptive instruction of teams