Persistence is considered a crucial factor for success in online learning environments. However, in interactive game-based learning environments, persistence in progressing in the game may come at the expense of investing in each of the game's levels. That is, the motivation to complete the game may have a deleterious effect on learning at specific levels and hence on learning from the game in general. Therefore, it is imperative that research focuses on micro-persistence, i.e., persistence during each component of the learning process. Taking a learning analytics approach, this large-scale log-based study (N=25,812 elementary- and middle-school students) examines micro-persistence within the context of learning computational thinking, a key skill for the 21st-century. Data was collected and analyzed from an online, game-based learning environment (CodeMonkey™). Results suggest that the acquisition of computational thinking is a multi-dimensional process, and that persistence is a crucial factor for success in multi-level game-based learning environments. The authors also found that game-based learning environments may prove effective in narrowing the gap between high-and low-achieving students.
TopIntroduction
Persistence, which is defined as the aspiration to complete a process and achieve its goals, has proven to be an important skill for learning and problem solving (Tran, 2019). However, achieving persistence is a significant challenge in online learning environments, due to various factors related to the learners, the settings and the context in which learning occurs (Hart, 2012). Game-based, interactive learning environments, which have become very popular in recent years, are associated with increased student motivation (Abdul Jabbar & Felicia, 2015; Buckley & Doyle, 2016), which is closely associated with persistence (Vollmeyer & Rheinberg, 2000). In such environments, persistence may come at the expense of investing in each of the game’s levels. That is, motivation to complete the game may harm learning at specific levels and, consequently, impair learning from the game at large.
Examining persistence at the macro-level — that is, measuring the completion of a course or a program — may not reveal the whole picture of knowledge acquisition. Thus, it has been suggested that persistence should be better measured for a single task by analyzing student time on task reports, and number of attempts for the activity (Cooley, Beaird, & Ayres, 1994; Multon, Brown, & Lent, 1991). This micro-level perspective enables data analysts to study micro-persistence, which is defined as persistence in each component of the learning process. Studying task-level persistence also bears the potential of predicting factors related to course completion (macro-level) based on factors related to a single task (micro-level). For example, success in a first-year college course was found to be best predicted by completion of homework assignments and average test scores (Syed et al., 2019); similarly, analysis of student log activity in MOOCs were found to be a strong predictor of student dropout (Gitinabard, Khoshnevisan, Lynch, & Wang, 2018; Xing & Du, 2019).
The purpose of this chapter is to examine micro-persistence within the context of computational thinking (CT). CT, which has been identified in recent years as an essential 21st-centruy skill, is a process where students define and solve computational problems. It is comprised of a large collection of mental strategies such as recursive and procedural thinking, modeling, abstraction and decomposition, heuristic reasoning, and parallelism (Wing, 2006, 2010).
In recent years, a growing number of user-friendly, computer-based environments have been developed to support the acquisition of CT skills (Burger, 2019; Kim & Ko, 2017). Some of these platforms have embraced the principles of game design, which create highly immersive, interactive game-like learning experiences, which lead to increased student motivation and improved learning outcomes (Ibanez, Di-Serio, & Delgado-Kloos, 2014; Kazimoglu, Kiernan, Bacon, & MacKinnon, 2012; Vu & Feinstein, 2017).
This chapter reports the findings of a large-scale study which examines the effects of micro-persistence on learning outcomes in game-based learning environments. The study utilized data collected from 25,812 elementary- and middle-school students who used CodeMonkey™ - a game-based learning environment – in winter 2018. This chapter describes the learning analytics method employed by the authors to analyze student micro-persistence, achievements, and success.