Team formation instruments to enhance learner interactions in open learning environmentsby Howard Spoelstra, Peter van Rosmalen, Tilly Houtmans, Peter Sloep

Computers in Human Behavior

About

Year
2015
DOI
10.1016/j.chb.2014.11.038
Subject
Psychology (all) / Human-Computer Interaction

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Text

le utm eerl alken

MOOCs

Social learning networks

Team formation model

Project-based learning

Project team formation ts, s s; th e le introduced to form productive, creative, or learning teams. These use data on the project and on learner knowledge, personality and preferences. A study was carried out to validate the principles and the ch as large ties for learner collaboration (Daniel, 2012; Edinburgh University, 2013; McGuire, 2013; Morrison, 2013). While there are many reasons for drop-out rates to be high, these effects can at least partly also be explained by learning settings that do not motivate learners. In the up till now small-scale connectivist MOOCs learners in open learning theoretical comamework o dividuals e problems, collaborate with peers to develop shared understa use and create learning materials, which are then again u others to learn from. While learning can be instigated by in als and whole communities can benefit from its outcomes, Stahl places the actual learning process in the context of the small group.

However, with regard to implementing the framework, Stahl (2013) also notes it: ‘‘. . . needs appropriate CSCL technologies, group methods, pedagogy and guidance to structure and support groups to effectively build knowledge . . .’’. In this article we investigate a particular approach to forming teams for collaborative ⇑ Corresponding author. Tel.: +31 (0)455762391.

E-mail addresses: howard.spoelstra@ou.nl (H. Spoelstra), peter.vanrosmalen@ ou.nl (P. van Rosmalen), tilly.houtmans@ou.nl (T. Houtmans), peter.sloep@ou.nl (P. Sloep).

Computers in Human Behavior 45 (2015) 11–20

Contents lists availab

Computers in Hu evibehaviourist, rather than social-constructivist educational principles. Reports, however, from both learners and MOOC providers indicate that drop-out rates from both kinds of MOOCs are massive, and that in particular the latter kind offers limited opportuniIn general, collaborative learning processes environments can take shape as suggested in the puter-supported collaborative learning (CSCL) fr (2006). Stahl describes that, in a cyclic process, inhttp://dx.doi.org/10.1016/j.chb.2014.11.038 0747-5632/ 2014 Elsevier Ltd. All rights reserved.f Stahl xpress nding, sed by dividu-tially these environments were envisioned to provide learning settings based on the pedagogical vantage point of networked learning, with a strong emphasis on learner self-direction and learner contribution. Downes (2006) and Siemens (2004) coined the term ‘‘connectivism’’ to label such learning settings. In parallel a different kind of MOOC rose to attention, one that builds on tunities are limited, which leads to sub-optimal learning (Daniel, 2012; Edinburgh University, 2013). Some MOOCs (NovoEd, 2014;

Stanford University, 2012) address this by allowing self-selection into teams or by providing relatively simplistic grouping criteria such as by proximity of geographic location or by language(s) mastered.1. Introduction

Open learning environments, su

Courses (MOOCs), currently attractalgorithms. Students (n = 168) and educational practitioners (n = 56) provided the data. The principles for learning teams and productive teams were accepted, while the principle for creative teams could not. The algorithms were validated using team classifying tasks and team ranking tasks. The practitioners classify and rank small productive, creative and learning teams in accordance with the algorithms, thereby validating the algorithms outcomes. When team size grows, for practitioners, forming teams quickly becomes complex, as demonstrated by the increased divergence in ranking and classifying accuracy. Discussion of the results, conclusions, and directions for future research are provided.  2014 Elsevier Ltd. All rights reserved.

Massive Open Online bodies of learners. Iniare expected to be self-directing, which can present learners with difficulties related to insufficient task structure (Kop, Fournier, &

Mak, 2011). In the large-scale behaviourism-based MOOCs, scaffolding, teacher-learner contacts and collaborative learning oppor-Keywords:

Open learning environments

MOOCS. To support definition and staffing of projects, team formation principles and algorithms areTeam formation instruments to enhance learning environments

Howard Spoelstra a,⇑, Peter van Rosmalen a, Tilly Ho aWelten Institute, Open University of the Netherlands, Valkenburgerweg 177, 6419 AT H b Faculty of Psychology and Educational Sciences, Open University of the Netherlands, V a r t i c l e i n f o

Article history: a b s t r a c t

Open learning environmen collaboration opportunitie learning (PBL) can enhanc journal homepage: www.elsarner interactions in open ans b, Peter Sloep a en, The Netherlands burgerweg 177, 6419 AT Heerlen, The Netherlands uch as Massive Open Online Courses (MOOCs), often lack adequate learner ey are also plagued by high levels of drop-out. Introducing project-based arner collaboration and motivation, but PBL does not easily scale up into le at ScienceDirect man Behavior er .com/locate /comphumbeh formation principles and by comparing practitioner outcomes on team formation tasks to the outcomes of the computer algorithms.

The remainder of the article is structured as follows: In Section 2 we present a team formation model, which uses learner knowledge, personality and preferences to suggest teams fit for executing a project. In Section 3, we present the research questions and hypotheses, on the basis of which we aim to validate the team formation instruments. Section 4 describes the materials and methods we used to test the hypotheses. In Section 5, the results are presented. Sections 6 provides an extensive discussion of these results, while in Section 7 we draw conclusions and suggest future research. 2. A team formation model

The automated service builds on earlier work in which we introduced a team formation model for use in open learning environments, as well as in more traditional learning settings. The model was constructed based on a review of PBL and team formaproject in domain