Unobtrusive Observation of Team Learning Attributes in Digital Learning

Abstract

This article presents a new framework for unobtrusive observation analytics of knowledge and skills-in-action through continuous collection of data from individuals while they interact with digital assets either as individuals or on problem-solving teams. The framework includes measures of the skill and knowledge areas of collaboration, creativity, personal learning, problem solving, and global sustainability, which are observed during natural production and use of communications, intentional artifacts, and resources in a digital learning space designed for self-directed and team-based learning challenges. The article describes the digital context for data collection and shows some example data and analyses.

Gamification for Teamwork Skills: Can a Challenge-based Online Tournament Help Students Learn New Knowledge Collaboratively in Teams?

“…In addition, the eTournament had provided game-like dynamics (Burke, 2014) for participants to earn rewards like scores, prizes as described in section 3.1 which seemed to motivate their participation as reflected by the number of 268 out of 416 participants qualified to proceed to Stage 2 of eTournament to play the challenging game of PaGamO. From the evidence presented in the previous sections, many participants seemed to be able to learn to work in virtual teams collaboratively through the different stages of the eTournament with increasing enjoyment in learning new knowledge (Gibson, 2018;Johnson & Adams, 2010). By relating the results obtained from Post-game Questionnaire, requiring participants to answer SDG-related questions on the challenge-based gamified learning platform PaGamO seemed to promote their awareness of the issues relevant to the SDGs.…”

“…Wang and Chen (2010) found that challenging games enable students more challenging and engaging in gaming activities. Therefore, the positive impact of using challenge-based approach gamification can improve student's engagement, expanding time working on tasks, and increasing satisfaction/enjoyment with learning (Gibson, 2018;Johnson & Adams, 2010). Suh et al (2016) reported that gamification could offer a wider variety of solutions that can better motivate students to learn collaboratively instead of individually.…”

Attributes of Engagement in Challenge-Based Digital Learning Environments

“…The Curtin Challenge platform is being developed to support both individual and team-based learning in primarily open-ended ill-structured problem solving and project-based learning contexts (Eseryel, Law, Ifenthaler, Ge, & Miller, 2014). The platform can also support self-guided learning, automated feedback, branching story lines, self-organizing teams, and distributed processes of mentoring, learning support and assessment (Gibson, 2018;.…”

Time to go wild: How to conceptualize and measure process dynamics in real teams with high-resolution

“…Yet, we noticed that high resolution research is particularly prominent in the context of action teams which have highly trained members operating under variable workload and uncertainty (Ishak & Ballard, 2012). Action teams can be found in a variety of industries: e.g., aviation (i.e., flight crews; e.g., Lei, Waller, Hagen, & Kaplan, 2016, Waller, 1999, healthcare (i.e., medical teams; Farh & Chen, 2018;Kolbe et al, 2014;Schmutz et al, 2015;2018;Zijlstra, Waller, & Phillips, 2012), crisis management (e.g., Stachowski, Kaplan, & Waller, 2009;Waller, Gupta, & Giambatista, 2004), and in professional sports (e.g., Grijalva, Maynes, Badura, & Whiting, 2019;Halevey, Chou, Galinsky, & Murningham, 2012;Stuart & Moore, 2017).…”

“…For examples how to transform archival sport team data into "team phenomena", see Halevy et al (2012) For examples/overviews how to transform electronic data from online teams, see Riedl & Wooley (2017), Gibson (2018) For overviews/tutorials how to use automatic text analytic approaches to transform text into team constructs, see Banks et al (2018), Bonito & Keyton (2018), Short et al (2018), Gonzales et al (2010). Available Tools for "Computer-Aided Text Analysis": http://www.amckenny.com/CATScanner/ http://liwc.wpengine.com/ For construct validity of data from wearables with surveys (in lab and field contexts), see Chaffin et al (2017) • Example: "The researchers code the videos with Co-Act (a validated scheme that captures team coordination).…”