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References

Citekey: @Rummel2016-wa

Rummel, N., Walker, E., & Aleven, V. (2016). Different Futures of Adaptive Collaborative Learning Support. International Journal of Artificial Intelligence in Education, 26(2), 784–795.

Notes

Summarize:

Assess:

Reflect:

Highlights

In this position paper we contrast a Dystopian view of the future of adaptive collaborative learning support (ACLS) with a Utopian scenario that – due to betterdesigned technology, grounded in research – avoids the pitfalls of the Dystopian version and paints a positive picture of the practice of computer-supported collaborative learning 25 years from now. (p. 784)

In particular, we see a need to work towards a comprehensive instructional framework building on educational theory. This framework will allow us to provide nuanced and flexible (i.e. intelligent) ACLS to collaborative learners – the type of support we sketch in our Utopian scenario. (p. 784)

The present paper focuses on an area where computer-supported collaborative learning (CSCL) and AIED research intersect: adaptive collaborative learning support (ACLS, see Walker et al. 2009b). (p. 784)

the 2014 IJAIED special issue entitled BIntelligent Support for Learning in Groups^ (ISLG) (p. 784)

In this paper, we consider two possible futures for the area of adaptive support for collaborative learning. First, we sketch a scenario that illustrates where ACLS might be 25 years down the road if technological solutions are not informed by educational theory and research. It is a scenario that we view as Dystopian, because the full potential that we see for adaptive and adaptable technologies has not been fully realized. (p. 785)

As a contrast we present a more optimistic, Utopian, scenario, one in which learners are empowered for collaborative learning by flexible, adaptive support that avoids the pitfalls of the Dystopian scenario. The Utopian scenario requires a challenging research agenda for the next 25 years. We pose that working towards a comprehensive instructional framework for ACLS can help to prevent the Dystopian and pave the way for a Utopian future of ACLS. (p. 785)

Fast-Forward to the Year 2040: a Dystopian Scenario (p. 785)

BOk,^ Mr. Roebeck said, Bget in your pairs and check your prescribed activity.^ (p. 786)

As they worked, the system continuously prompted, BJanet, can you explain your reasoning?^ BJanet, what do you think about what Roxanne just said?^ (p. 786)

A while back, RUWAAL had diagnosed her as a Bpoor collaborator.^ Mr. Roebeck had told her parents that she would be getting remediation (p. 786)

Unpacking the Dystopian Scenario (p. 787)

The range of pedagogical decisions that is supported by or taken over by technology will likely expand compared to what is available today: automatic group formation, selection of activities for each group, and adaptive prompting and scripting to ensure learners are collaborating and learning effectively. (p. 787)

Nevertheless, there is much that is unsatisfying about this scenario. The system makes decisions about what will lead Janet to learn most effectively based on data about her, and other students’, current and past activities, but these decisions lack nuance and flexibility, leading to frustration and ultimately to a lack of trust in the system. (p. 787)

A key shortcoming of RUWAAL is that the different dimensions of support in its ACLS are not well coordinated and thus are not working together in a coherent, holistic way (p. 787)

A related shortcoming evident from the Dystopian scenario is that the system’s pedagogy is very limited. (p. 787)

A final problem is that RUWAAL’s decisions are inscrutable to learners and teachers. (p. 788)

A Contrasting View: a Utopian Scenario for 2040 (p. 788)

Unpacking the Utopian Scenario (p. 789)

Its decision making is driven by theory, takes into account a variety of pedagogical goals (including learning at the domain level and learning of collaboration skills), and draws on a wide arsenal of pedagogical strategies. It coordinates support across multiple (p. 789)

dimensions (e.g., timing of support, psychological realm of support, mode of support, and support type), enabling it to flexibly adapt to students’ needs. Finally, the system is adaptable: It is transparent, works synergistically with students and teachers, and shares control with them. (p. 790)

The ACLS system’s balanced theory-driven integration of multiple dimensions of support for collaborative learning is on display when our student (Janet) spontaneously starts to self-explain a difficult chemistry concept. (p. 790)

The scenario also illustrates the importance of creating technology that is adaptable. (p. 790)

A Research Agenda for 2016-2040 (p. 791)

The differences between our two scenarios exemplify the dilemmas that designers of ACLS systems face. The kind of coordinated pedagogical decision making illustrated in the Utopian scenario is a tough balancing act, even for humans, but one that the area of ACLS will need to tackle. (p. 791)

Taking into account the issues identified in unpacking the Dystopian scenario, we see a need to work towards a comprehensive instructional framework firmly rooted in educational theory that can guide the development of nuanced and flexible ACLS systems. (p. 791)

The proposed research can build on prior work in ACLS, which has produced several taxonomies of support for collaborating students. These taxonomies (e.g. Diziol and Rummel 2010; Walker et al. 2009a) identify dimensions such as the timing of support (whether it is provided immediately or with some delay during the collaboration, or before or after the collaboration), the psychological realm of support (cognitive, social, metacognitive, motivational, affective), the mode of support (explicit or implicit), the locus of support (direct or indirect), the target of support (group formation, domain knowledge, peer interaction, social skill; Magnisalis et al. 2011), and the type of support (guiding, challenging reflection, mirroring; Soller et al. 2005). (p. 791)

To move towards the aspired instructional framework, we must carry out rigorous empirical research and engage in related theory-building efforts. First, we need research within each individual support dimension. (p. 791)

Secondly, let us consider the dimension of mode of support. (p. 791)

For example, Kumar et al. (2007) found that students tended to ignore adaptive prompts while collaborating. (p. 792)

A third substantial challenge is to decide on the psychological realm of support to be targeted in given circumstances. Several realms can be targeted, beyond domain-level support: for instance, metacognitive support, motivational support, and support at the social level (e.g., Muldner et al. 2010; Ogan et al. 2010; Roll et al. 2011). (p. 792)

A further desirable aspect of the aspired instructional framework is to allow us to better balance system adaptivity and feedback with user choice and freedom. A focus on optimizing adaptivity of ACLS runs the risk of overemphasizing the role of the system in guiding the students, rather than empowering students and teachers to make good pedagogical choices with the help of the system. More research effort should be dedicated to the adaptability of ACLS systems (i.e. the possibility for users to adjust the collaborative learning situation to their current needs or goals by making active choices). (p. 793)

Conclusion (p. 794)

The instructional framework we envision will be detailed and precise enough to facilitate the implementation of nuanced, highly adaptive support in educational software. Development of this framework encompasses three components: 1. The integration of theories of collaborative learning to create a comprehensive framework that, alongside data-driven models, can inform support; 2. The derivation of principles of when, how, and what support to provide, which integrate the dimensions of support, so that decisions can be made in a coordinated and transparent manner; 3. The derivation of methods for balancing adaptivity and adaptability within support. (p. 794)

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