Citekey: @Rose2016-rs

Rosé, C. P., & Ferschke, O. (2016). Technology Support for Discussion Based Learning: From Computer Supported Collaborative Learning to the Future of Massive Open Online Courses. International Journal of Artificial Intelligence in Education, 26(2), 660–678.






Thisarticleoffersavisionfortechnologysupportedcollaborativeanddiscussionbased learning at scale. (p. 660)

In particular, this support has been enabled by an integration of text mining and conversational agents to form a novel type of micro-script support for productive discussion processes. (p. 660)

In the next 25 years, we expect to see this early, emerging work in MOOC contexts grow into ubiquitously available social learning approaches in free online learning environments like MOOCs, or what comes next in the online learning space. These ambitious social learning approaches include Problem Based Learning, Team Project Based Learning, and Collaborative Reflection. We expect to see the capability of drawing in and effectively supporting learners of all walks of life, especially impacting currently under-served learners. (p. 660)

Introduction (p. 661)

Across theoretical frameworks in which learning is studied, including behaviorist, cognitive constructivist, and sociocultural perspectives, conversation is valued (Hmelo-Silver et al. 2013; Resnick et al. 2015). (p. 661)

diff w/ discourse? (p. 661)

A growing interest and involvement in development of technologies to support discussion for learning has been represented within the AI in Education community over the past two decades. Beginning in the mid-90s, this interest initially focused mainly on the area of tutorial dialogue systems to support individual learning (Evens and Michael 2006; Aleven et al. 2003; Zinn et al. 2002; VanLehn et al. 2002) and gradually included more and more emphasis over the past decade on computer-supported collaborative learning supported by conversational agents and other dynamic support technologies (Kumar et al. 2007; Kumar and Rosé 2011; Dyke et al. 2013; Adamson et al. 2014). (p. 661)

Though there are almost always discussion forums included in these environments, they are often just an appendage, and not effective in meeting the needs of learners, especially under-served learners who need more support. (p. 661)

Historical Foundations (p. 662)

Tutorial Dialogue (p. 662)

expert human tutors are highly successful at educating students (Bloom 1984; Cohen et al. 1982). (p. 662)

Emulating this B2 sigma effect^ has long been the holy grail of intelligent tutoring research. (p. 662)

The search for the answer to the elusive B2 sigma effect^ has taken many forms, but one common thread through generations of investigation has been the belief that the answer lies in the natural language dialogue that is the dominant form of communication between students and human tutors. (p. 662)

Thus, in recent years a growing amount of attention turned to the use of conversational agents as facilitators in collaborative learning interactions (p. 662)

carefully designed agents (Kumar et al. 2007; Dyke et al. 2013; Adamson et al. 2014). From a different angle, some recent work still targeting individual learning with technology has sought to leverage the same feel of a group interaction through multiple agents interacting with individual students in what are referred to as trialogues (Graesser et al. 2014). (p. 663)

Computer-Supported Collaborative Learning (p. 663)

This earlier work, referred to as scripted collaboration, has been a major focus of the field of Computer-Supported Collaborative Learning in the past decade, and despite its limitations, has produced numerous demonstrations of its effectiveness in improving collaborative learning. (p. 663)

Context-sensitive or need-based support necessitates on-line monitoring of collaborative learning processes. Automatic analysis of collaborative processes is an advance in the field of Language Technologies that has value for real time assessment during collaborative learning, for dynamically triggering supportive interventions in the midst of collaborative-learning sessions, and for facilitating efficient analysis of collaborativelearning processes. (p. 663)

Frameworks for analysis of group knowledge building are plentiful and include examples such as Transactivity (Berkowitz and Gibbs 1983; Teasley 1997; Weinberger and Fischer 2006), Intersubjective Meaning Making (Suthers 2006), and Productive Agency (Schwartz 1998). (p. 663)

text-based interactions and key-click data — match with what we are writing in the proposal. (p. 663)

text-based interactions and key-click data (Soller and Lesgold 2000; Erkens and Janssen 2006; Rosé et al., 2008; McLaren et al. 2007; Mu, Stegmann, Mayfield, Rosé, & Fischer, 2012). (p. 663)

audio data has begun (Gweon et al. 2013) (p. 663)

Moocs: The Bleeding Edge (p. 664)

If we can improve the experience of community and social support around MOOCs, we may potentially decrease attrition for the current ilk of MOOC participants. (p. 664)

The opportunity for impact is great if we look past the top tier and other four year institutions of higher learning, and instead target community college level instruction.1 (p. 664)

Creating a supportive environment in which these learners can find community, support, dignity, and respect is essential for achieving this impact. (p. 664)

Dual-Layer Moocs: A Case Study (p. 664)

It was termed a Bdual layer^ MOOC because students had the option of following a more standard path housed in the edX platform for moving through the course in one layer or to follow a more self-directed path in an environment called ProSolo,2 developed by Gašević and colleagues that formed a second layer. (p. 665)

The purpose of the ProSolo integration was two-fold a) increasing the social learning experiences of self-regulated learners in MOOCs through social competency approaches to learning and b) developing learner knowledge graphs that reflect what a learner knows and how they have come to know it. (p. 665)

Learning in ProSolo is social in the sense that students follow and communicate with other students during the process of setting their learning objectives. However, beyond that, the learning experiences students engaged in within the ProSolo environment are still largely individual in character. (p. 665)

In that context, we deployed two different interventions leveraging advances in Language Technologies such as automated analysis of discussion behavior to trigger dynamic support (using an unsupervised matrix factorization approach), including what we termed the Quick Helper and a direct import of our agent supported collaborative learning technology, which we referred to as Bazaar Collaborative Reflection in this context. (p. 665)

Over the next 25 years, we expect to see social learning practices supported more effectively at scale, overcoming coordination challenges, and provided ubiquitously within free learning environments, such as MOOCs. (p. 665)

The Quick Helper (p. 665)

to support help seeking as well as increase the probability that help requests will be met with a satisfactory response. (p. 666)

Our help seeking intervention connects students, whose questions may go unresolved, with student peers who may be able to answer their question. (p. 666)

This approach is the first step towards bringing peer help recommendation into the MOOC space considering the unique characteristics of MOOCs such as the relatively weak social bonds between students, the great diversity among students, the lack of knowledge about specific students, and the wide range of motivations for students to participate in a MOOC which influences their possible roles as help providers. Future progress in user modeling based on the noisy MOOC data and taking into account the social ties between users within and outside of the MOOC platform will allow us move towards more sophisticated help recommendation approaches as proposed and achieved using knowledge based approaches in related work on online help recommendation for on-campus education such as i-Help (Greer et al. 1998; Vassileva et al. (p. 666)

Bazaar Collaborative Reflection (p. 667)

Another intervention, referred to as Bazaar Collaborative Reflection, makes synchronous collaboration opportunities available to students in a MOOC context. (p. 667)

Using the number of clicks on videos and the participation in discussion forums as control variables, we found that the participation in chats lowers the risk of dropout by approximately 50 % (Ferschke et al. 2015b). (p. 667)

Into The Future (p. 667)

The use of social recommender systems (such as Quick Helper) and group collaboration tools (such as Bazaar) are expected to lead to higher levels of metacognitive monitoring, which we expect to be associated with an increase of confidence, feeling of knowing, judgment of learning, and monitoring of progress toward goals. (p. 667)

The goal is both to design and build platform extensions to support effective learning, but also to provide new opportunities to study learning through discussion in new ways and in novel contexts, for example, addressing important questions related to learning in highly diverse collaborative groups. (p. 668)

Projecting the inspiration for our Bdual layer MOOC^ into the future, we envision MOOCs of the future as gateways into persistent Communities of Practice. (p. 668)

good point
A good point worth considering in the context of PKU MOOCs! (p. 668)

Decades of research studying the inner workings of these communities, whether focused on learning, work or a hybrid of the two, has revealed that a central problem underlying the success of these efforts is channeling the attention and efforts of the masses. This channeling may partly be achieved through facilitation of consensus and coordination achieved through a careful balance of guidance and self-direction. On the other hand, unless this is balanced with an effort to avoid premature consensus, group think, or abandonment of valuable ideas that may simply have been overlooked, support for coordination and consensus building could hinder the development of creativity or the growth of innovation. (p. 669)

A proposed solution to this complex and multi-faceted challenge is to provide noncoercive personalized guidance through awareness tools that reveal the shape and foci of the behavior data and resulting byproducts in a way that makes it natural for collaboration to grow out of individual decision-making. We have begun to lay a foundation for this work through technology that is able to identify role-based behavior profiles associated with productive group and community outcomes. (p. 669)

Instead through a theory guided but data driven process, we identify which configurations of behaviors are achieving positive outcomes, and then work to foster greater representation of these behavior profiles. (p. 669)

However, many challenges exist: traditionally, Knowledge Forum has been mainly used in classroom settings, and there is much to learn as it is (p. 669)

adapted for use in a MOOC context. In Knowledge Building classrooms, the teacher plays an important role setting the culture in which Knowledge Building can take place. How can we replicate that in a MOOC? One question is related to structuring interaction. cMOOCs are loosely structured, perhaps more so than the approach to Knowledge Building that has been done in classrooms. The design space is wide open. On one end of the spectrum one might imagine a Knowledge Building MOOC, which would be Ba MOOCified Knowledge Forum^. (p. 670)

On the other end of the spectrum, elements of Knowledge Building might be made available within courses structured in more typical learning progressions and employing other pedagogies as well. A third option might be to introduce the Knowledge Forum approach into an environment where large scale collaboration is already happening for producing new knowledge, such as wikis. In this paradigm, wiki activities would be embedded in MOOCs with support for knowledge building scoped within the wiki. Another issue to consider is the role of the instructor. (p. 670)

The Role Of Natural Language Processing (p. 670)

Long time members of the computational linguistics community have observed the paradigm shift that took place after the middle of the 1990s. Initially, approaches that combined symbolic and statistical methods were still of interest (Klavans and Resnick 1994). However, with the increasing focus on very large corpora and leveraging of new frameworks for large scale statistical modeling, symbolic and knowledge driven methods for natural language processing were largely left by the wayside. (p. 670)

On the positive side, the shift towards big data came with the ability to build real world systems relatively quickly. However, as knowledge-based methods were replaced with statistical models, a grounding in linguistic theory grew more and more devalued, and instead a desire to replace theory with an almost atheoretical empiricism (p. 670)

But over time, an awareness began to grow that social data had characteristics on its own, and new methods would be needed in order to achieve adequate results. It was at this point that interest in computational social science as the answer to this need began to arise. (p. 671)

Atheoretical empiricism is not attractive within the behavioral sciences, including the learning sciences, where the primary value is on building theory and engaging theory in interpretation of models. In the learning sciences, process measurements must be motivated in the theory in order to be valid. (p. 671)

At a very basic level, work within the area of computational sociolinguistics draws from social psychological concepts of relative power in social situations (Guinote and Vesvio 2010), in particular aspects of relative power that operate at the level of individuals in relation to specific others within groups or communities. At this level, relative power may be thought of as operating in terms of horizontal positioning, which relates to closeness and related constructs such as positive regard, trust and commitment, or vertical positioning, which relates to authority and related constructs such as approval and respect. (p. 671)

At a conceptual level, this work draws heavily from a foundation in linguistic pragmatics (Levinson 1983) as well as sociological theories of discourse (Gee 2011). (p. 671)

While this very general framing provides an umbrella under which work within this area can be placed, the specific framing of individual publications varies substantially. At one end of the spectrum, some contemporary publications in the Language Technologies field are motivated purely from within the Language Technologies literature with a focus either on categories of conversational behavior defined in earlier research (Zhai and Williams 2014; Hasan and Ng 2014; Nguyen et al. 2012), or framed from purely a task related perspective (Bhatia et al. 2014) or a modeling perspective (Paul 2012). (p. 672)

In recent years, a substantial amount of work in this area has leveraged or at least references theoretical frameworks from Social Psychology (Bak et al. 2014; Bracewell et al. 2012). (p. 672)

As this bridging between fields continues to grow, we are in a better position to use theoretical insights into the deep inner workings of language to motivate design decisions that underlie the computational models we build. These insights have the potential to yield greater validity in the work on learning analytics applied to conversational interaction data. (p. 672)

Reflections (p. 673)

Rather than regarding MOOCs as scaled-up virtual classrooms, they should be compared to interactive textbooks around which communities of practice are formed that engage in collaborative learning and cooperative work driven by a common interest. (p. 673)

The current generation of MOOCs have sometimes been referred to as the Alta Vista of MOOCs, and we have not yet seen the Google Search of MOOCs. By the end of the next 25 years, we expect that the very idea of a MOOC per se will have faded into a distant memory, and will be replaced with a new, far more effective paradigm for learning at scale. Our vision is that this paradigm will be one where ambitious social learning practices will be ubiquitously offered, such as Problem Based Learning, Team Based Learning, Collaborative Reflection, and spontaneous personalized mentoring. These new environments will be active, thriving, communities where students are supported on their personal learning path. (p. 674)

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