Team Formation Services for Self-Directed Learners in Learning Networks

Supporting Project Team Formation for Self-directed Learners

Howard Spoelstra1, Peter van Rosmalen1 and Peter B. Sloep1

1 Centre for Learning Sciences and Technologies (CELSTEC), Open University of the Netherlands, Valkenburgerweg 177, 6419AT Heerlen, The Netherlands {howard.spoelstra, peter.vanrosmalen, peter.sloep}@ou.nl

Abstract. The outcomes of project-based learning can be optimized if team formation experts assemble the project teams. For self-directed learners in globalized online learning settings human team formation experts might be unavailable. The question is addressed about how to provide team formation services for such learners. A model of the team formation process is defined, based on current team formation theory. It is used to design an automated team formation service that can be used by self-directed learners to form teams for project-based learning. Starting from a project description situated in a knowledge domain, the model defines three categories of variables that govern the team formation process: knowledge, personality and preferences. Learners’ scores on these categories are analyzed and combined into scores on the new concept of a learners’ “project suitability”, on the base of which teams can be suggested. A discussion of the outcomes and an overview of future research are included.

Keywords: project-based learning, project team formation, self-directed learning, learning networks, team formation service, computer supported group formation 2 Howard Spoelstra1, Peter van Rosmalen1 and Peter B. Sloep1

Introduction

In our quest to inform ourselves the Internet has given us the ability to connect to people all over the world. But does the Internet also optimally support more focused activities such as lifelong learning for geographically distributed learners? Supporting lifelong learners in distributed settings differs from supporting learners in initial education [21], because of the learner’s self-directed and unsupervised learning behavior [2]. Furthermore, support services are not readily available to self-directed lifelong learners, and thus have to be designed. On these premises, we present a design for a service that supports team formation for project-based learning. For project-based learning to deliver optimal results, experts should form the teams [16][5][4][15]. In our design, the novel concept of a learner’s project suitability is introduced as an inter-learner measure of fit of the combination of knowledge, personality and preferences to a specific project, on the basis of which teams can be formed. A summary of our work so far and an indication of forthcoming research are included.

Project-based Learning and Team Formation

Project-based Learning

Compared to individual learning, collaborative learning leads to an improvement of learning outcomes [7]. A number of reasons for this improvement are given below [22][3]:

─ the direct engagement with the subject matter

─ the increased socialization and exposure to different ideas, and Supporting Project Team Formation for Self-directed Learners 3

─ the level of information processing intensifies.

Naturally, collaborative learning can only occur when learners operate in teams, which can be defined as “a group of people working together on a well-defined task or set of tasks” [17].

Team Formation

To form effective teams, a team formation expert requires knowledge of the prospective team members and their tasks. The most important variables in this knowledge are [6][14][20][25]: a) the individual learner’s knowledge and prior learning achievements b) the individual learner’s personality traits, personal skills, gender, personal interests and motivational orientation c) the curriculum area in which the project task will be positioned d) the project task itself and, as a derivative, the team size related to the duration of the project and the work that has to be done.

However, the question is how the knowledge about these variables can be acquired and fitted together to form teams. For instance, to foster learning in teams, complementary fit in knowledge background should be paired with supplementary fit in personality [10][23]. A number of team formation applications have already been developed to help the expert. These computer supported group formation applications use such variables as: domain knowledge, learning goals, performance in previous teamwork, specific expertise, preferred time slots and preferred projects, performance and personality traits (see 4 Howard Spoelstra1, Peter van Rosmalen1 and Peter B. Sloep1 e.g., [18] for an overview). However, systems that are aimed at specific learning situations limit the possible differentiation into varied project- based activities outcomes (e.g., increased productivity, creativity or learning outcome). We aim to develop a new and more general approach to the team formation process. The result should meet the needs of lifelong learners in their ill-defined learning settings.

Approach and the Model of the Team Formation Process

We determined that most of the team formation process variables related to the prospective team members (as identified in section 2) can fit into two categories:

─ Knowledge (used here in a broad sense, as we refer to the knowledge about all the variables mentioned in points a, c and d), and

─ Personality (used here in a broad sense, e.g., including personal skills, as we refer to all the person-related variables mentioned in point b).

However, for distributed lifelong learners in their ill-defined online learning settings (see text box 1) obviously a third category of variables is relevant:

─ Preferences (they include variables like possible collaboration language(s), an availability schedule and preferred collaboration tools). Supporting Project Team Formation for Self-directed Learners 5

Learning Networks (LNs, [9]) are distributed online learning settings aiming to support lifelong learning. They are designed to promote learning through bottom-up, self-organized clusters of communities by enabling learner self-service and self-direction [2]. LN users tend to have more heterogeneous backgrounds (with respect to e.g., age, culture, language, knowledge, job or country of residence [21]. Currently LNs mainly support individual learning.

Textbox 1: Learning Network settings

Using these categories of variables, a model was constructed that places the project team formation process in project-based learning settings.

The Model for the Team Formation Process

Chronologically, team formation in project-based learning starts with the definition of a project related to a part of a knowledge domain (e.g., a curriculum). The complementary and supplementary fit principles are used to determine the fit of prospective members on knowledge, personality and preferences to the project. This results in a prospective member’s “project suitability”, defined as “an inter-learner measure of fit of the combination of knowledge, personality and preferences for a specific project”. The team formation process ends with a suggestion for a project team when a set of project-suitable members can be found that show optimum fit. 6 Howard Spoelstra1, Peter van Rosmalen1 and Peter B. Sloep1

Project Domain

Assessment of knowledge Assessment of personality Assessment of preferences For complementary fit For supplementary fit For supplementary fit

Project suitability

Project team

Fig. 1. The model for the team formation process. Projects are defined inside a knowledge domain; prospective team members’ knowledge, personality and preferences determine their project suitability, on which team formations are proposed.

However, situating this team formation process model in an ill-defined learning context (like a LN) raises the question of how project-based learning and the team formation process should be started. We propose to design an automated self-service team-formation expert by proxy, based on the team formation process model discussed (see figure 1). We refer to this proxy as “team formation service”.

The Design for the Team Formation Service

The design for the team formation service needs to consider the effects of self-direction, self-service and the learning settings. Main issues are: a) automated team formation services can only be based on explicit data on the domain, the project task, the learner’s knowledge, personality and preferences, b) it is a learner that starts the project, and Supporting Project Team Formation for Self-directed Learners 7 c) the project is not necessarily positioned in a well-defined curriculum. The assessment of the knowledge required and knowledge available will be deferred to a knowledge proxy and the assessment of personality and preferences will be handled by a personality and preferences proxy. The results of these assessments then are combined into a score for the learner’s project suitability. The knowledge proxy operates on available textual materials: a) the learning materials in the domain, b) a project description and c) the knowledge evidence provided by the learners. Tools for textual analysis, such as Latent Semantic Analysis (LSA) [12] [13], can perform the automated analysis. LSA is used to create a representation of the knowledge in the domain in a vector space. The vector space can be queried with the project description in order to reveal the semantic similarities to documents in the vector space, thus showing which knowledge is required to perform the project. When queried with e.g., the learner CV it reveals the CV’s similarity to the documents in the vector space, thus showing which knowledge is available from the learner. The results of both analyses can then be compared; jointly, the results provide an indication of what knowledge is required for the project, and what knowledge each learner provides. Figures 2a, b and c paint a simplified picture of the LSA analysis results for the team formation process. In figure 2a, the four vectors (A, B, C, and D) indicate that the project description refers to the content of four particular documents represented in the domain vector space. In the figures 2b and 2c the three vectors each indicate the knowledge available at the learners “b” and “c”. Learner “b” shows knowledge on 8 Howard Spoelstra1, Peter van Rosmalen1 and Peter B. Sloep1 documents A, B and C, while learner “c” has knowledge on the documents A, B and D. The length of the vectors indicates to which extent the learners have knowledge equivalent to documents represented in the vector space.

Fig. 2a, b and c. 2a: The result of an analysis of a project description showing to which documents (the direction of the vector) and to which extent (the length of the vector) the project refers to documents in the domain vector space. 2b: The results of an analysis of knowledge evidence provided by a learner “b”, showing to which documents and to which extent the evidence refers to documents in the domain vector space. 2c: Results of the same analysis of knowledge evidence provided by a learner “c” (adapted from [11]).

The personality and preferences proxy takes a different approach in that it uses the personality and preferences data from a learner profile. The design of the personality profile will include variables that predominantly predict a person’s future performance in a team. These variables are generalised in a person’s conscientiousness [26] [27]. Conscientiousness is defined as a function of a person’s carefulness, thoroughness, sense of responsibility, level of organisation, preparedness, inclination to work hard, orientation on achievement and Supporting Project Team Formation for Self-directed Learners 9 finally, perseverance. The learner score is established by analysing questionnaire data on conscientious behaviour [8]. A learner’s preferences are established in the preferences profile, which includes variables such as availability, preferred collaboration language and tools. The team formation service combines all these scores and suggests a possible team, by matching learners that have knowledge of the required subjects, show similar levels of conscientiousness and have overlapping preferences. This design offers the ability to steer the desired outcomes of the project-based activities towards e.g., improving learning, productivity or creativity by varying the degree to which prospective team members have to show fit in the knowledge and personality category [19][24][1].

Discussion and future research

We identified several learner-related variables in the team formation process and showed how they fit into three categories: knowledge, personality and preferences. The principles of complementary and supplementary fit are used to construct a model of the team formation process for project-based learning. From this a self-service team formation service was defined, which could use LSA-technology to determine required and available knowledge, and evaluates learner profiles on conscientiousness and preferences to suggest teams. Different weighting schemes for knowledge and personality fit allow biasing project outcomes. Future research: Our first aim is to validate the design principles and the proposed model by verifying the variables that play a role in team 10 Howard Spoelstra1, Peter van Rosmalen1 and Peter B. Sloep1 formation process, and determining the relevance of the categories of variables we identified (also in relation to each other). We will do so by interviewing experts and asking them about their practice and their response to the proposed model. Next, we aim to build the team formation service and validate it through experiments.

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