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

Constructive interaction in scripted computer-supported collaborative learning

Abstract. This study explores the constructive interaction of higher education students during the Facebook groups’ discussion. The specific aims are investigating what forms of interaction were generated and how these interactions vary in three differently supported scripts. The participants of this study were ten small groups of higher education students (N=88) from three different Universities; collaborative learning for these groups was supported with a particular design micro- script for promoting both participation towards task-related and socio-emotional interaction over a six-week CSCL course. The results show that constructive interaction was rarely found. The majority of groups manifested more in the task-related than the socio-emotional categories. In terms of differences within the three collaboration phases, the intense constructive interaction was shown in the first and second tasks, where scripts were still supported students’ collaborative activities. Based on the findings, it can be concluded that the group who actively contributed to socio-emotional interaction was likely to engage well in task-related performance

Developing a New Instrument to Assess Online Learners' Sense of Community in Computer-Supported Collaborative Learning Environments

The purpose of this study is to provide validity evidence for the measurement model underlying a new assessment designed to assess online learners’ sense of community in computer-supported collaborative learning communities (SoC in CSCL). A two-level measurement model was proposed based on a comprehensive literature review. The first level included four perceptual constructs and the other level contains eleven instruction-related factors. In the pilot study, 206 students taking online courses at one university participated. Combination of Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) was used to refine the measurement model and the instrument. Two perceptual constructs, seven instruction-related factors, and 24 items were left. Results showed acceptable model fit (χ2 = 409.386, df = 209; RMSEA = .068, CFI = .945, TLI = .927) and adequate reliability (α = .944 and ω = .957) for the refined measurement model. In the replication study, 192 online students participated. Results showed acceptable model fit (χ2 = 436.861, df = 207; RMSEA = .076, CFI = .942, TLI = .922) and adequate reliability (α = .961, ω = 967) again in a new sample. Overall, results indicate that online learners’ sense of community is concerned with their feeling of membership and fulfillment of need in that community. Seven instruction-related factors can also account for online leaners’ sense of community in CSCL environments. The measurement model functions as a reference for online educators to understand online learners' perceptions and needs in CSCL communities and design specific instructional interventions to facilitate learners’ interaction, collaboration, and productivity in online learning environments

Enhancing Free-text Interactions in a Communication Skills Learning Environment

Learning environments frequently use gamification to enhance user interactions.Virtual characters with whom players engage in simulated conversations often employ prescripted dialogues; however, free user inputs enable deeper immersion and higher-order cognition. In our learning environment, experts developed a scripted scenario as a sequence of potential actions, and we explore possibilities for enhancing interactions by enabling users to type free inputs that are matched to the pre-scripted statements using Natural Language Processing techniques. In this paper, we introduce a clustering mechanism that provides recommendations for fine-tuning the pre-scripted answers in order to better match user inputs

Developing a New Instrument to Assess Online Learners' Sense of Community in Computer-Supported Collaborative Learning Environments

The purpose of this study is to provide validity evidence for the measurement model underlying a new assessment designed to assess online learners’ sense of community in computer-supported collaborative learning communities (SoC in CSCL). A two-level measurement model was proposed based on a comprehensive literature review. The first level included four perceptual constructs and the other level contains eleven instruction-related factors. In the pilot study, 206 students taking online courses at one university participated. Combination of Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) was used to refine the measurement model and the instrument. Two perceptual constructs, seven instruction-related factors, and 24 items were left. Results showed acceptable model fit (χ2 = 409.386, df = 209; RMSEA = .068, CFI = .945, TLI = .927) and adequate reliability (α = .944 and ω = .957) for the refined measurement model. In the replication study, 192 online students participated. Results showed acceptable model fit (χ2 = 436.861, df = 207; RMSEA = .076, CFI = .942, TLI = .922) and adequate reliability (α = .961, ω = 967) again in a new sample. Overall, results indicate that online learners’ sense of community is concerned with their feeling of membership and fulfillment of need in that community. Seven instruction-related factors can also account for online leaners’ sense of community in CSCL environments. The measurement model functions as a reference for online educators to understand online learners' perceptions and needs in CSCL communities and design specific instructional interventions to facilitate learners’ interaction, collaboration, and productivity in online learning environments

Together we stand, Together we fall, Together we win: Dynamic Team Formation in Massive Open Online Courses

Massive Open Online Courses (MOOCs) offer a new scalable paradigm for e-learning by providing students with global exposure and opportunities for connecting and interacting with millions of people all around the world. Very often, students work as teams to effectively accomplish course related tasks. However, due to lack of face to face interaction, it becomes difficult for MOOC students to collaborate. Additionally, the instructor also faces challenges in manually organizing students into teams because students flock to these MOOCs in huge numbers. Thus, the proposed research is aimed at developing a robust methodology for dynamic team formation in MOOCs, the theoretical framework for which is grounded at the confluence of organizational team theory, social network analysis and machine learning. A prerequisite for such an undertaking is that we understand the fact that, each and every informal tie established among students offers the opportunities to influence and be influenced. Therefore, we aim to extract value from the inherent connectedness of students in the MOOC. These connections carry with them radical implications for the way students understand each other in the networked learning community. Our approach will enable course instructors to automatically group students in teams that have fairly balanced social connections with their peers, well defined in terms of appropriately selected qualitative and quantitative network metrics.Comment: In Proceedings of 5th IEEE International Conference on Application of Digital Information & Web Technologies (ICADIWT), India, February 2014 (6 pages, 3 figures