Exploring Cloud Computing Tools to Enhance Team-Based Problem Solving for Challenging Behavior

Abstract

Multi-tiered systems of support (MTSS) provide frameworks to guide the instructional decisions and problem solving of educators (i.e., Sugai & Horner, 2009). Within MTSS, the availability of ongoing child assessment and progress monitoring data is described as a critical feature (Buzhardt et al., 2010; Sugai & Horner, 2009). For some time, there was an assumption that if tools were created to gather information about children's proficiency with and progress in developing essential learning targets, educators would be equipped to effectively engage in two essential components of instruction, data-driven decision making and ongoing problem solving (Hoogland et al., 2016; Marsh & Farrell, 2015; Todd et al., 2011). Unfortunately, multiple researchers have demonstrated that despite the availability of tools for gathering data, those data do not always lead to decision making about instructional practices that are in service of improving child outcomes (Heritage, Kim, Vendlinski, & Herman, 2009; Olah, Lawrence, & Riggan, 2010). The purpose of this article is to explore one approach to trying to address the problems associated with data-driven decision making in early childhood through use of cloudbased tools for gathering and summarizing data. To begin that exploration, a brief description will be provided that grounds the work in critical efforts to address the social, emotional, and behavioral development of young children. The complexities of the work in early childhood will then be described as a means for considering why existing tools may not be sufficient when promoting data-based problem solving. Then, a model for team-based problem solving will be described as a necessary approach to building knowledge about the use of data that meaningfully supports instructional decisions. Finally, a cloud-based knowledge management system will be described, along with the experience of a group of early childhood programs using the system, to demonstrate the potential benefits that that type of system may offer when the goal is to promote actual use of data, not just the gathering of data. Actualizing the use of data for effective decision making is a multifaceted issue. In addition to the barriers that are often expected during the installation of new practices, such as time and expertise (Anderson,

Effects of a Mobile Cloud-Based Learning System Using a P2P Reverse Engineering Approach on Enhancing Computational Thinking

“…The results were consistent with Johnson's [37] study, which found that integrating cloud technology into learning helped increase the scores at a significance level of .05. Moreover, the study showed that cloud technology integration helped develop students' potential for learning and solving problems collaboratively and promoted students' higher-order thinking skills.…”

“…Mobile learning is defined as the use of applications installed in a mobile device (tablet, smartphone, or laptop) for learning purposes in educational institutions, such as schools or universities. Mobile cloud-based learning has intriguingly facilitated the learning process, representing the mobile learning model and providing users with useful learning materials [37]. Compared with other forms of learning, learning through mobile devices is more private and has more potential for connected collaborative activities, despite being in the same learning environment [17], [19].…”

Rater behaviors in peer evaluation: Patterns and early detection with learner model

“…Additionally, in the data-driven environment, previous rounds' learning log data empowers teachers to implement targeted interventions (Johnson, 2017).…”

Data-Driven Support Infrastructure for Iterative Team-Based Learning

“…Many studies in collaborative learning field have primarily focused on utilizing learner attribute data to predict performance [19], aid decision-making for incident detection [20], and to provide formative feedback through data visualization [21], [22]. For instance, the significance of domain knowledge has been emphasized in problem-solving tasks that require high knowledge construction [23], [24].…”

“…When implemented in a data-driven environment, the learning log data from previous rounds empowers teachers to deliver targeted interventions [20]. With the increasing accumulation of learning log data, recent CSCL studies have opened avenues for supporting collaborative learning with state-of-the-art technical frameworks and data-rich environments [2].…”