Sustaining iterative game playing processes in DGBL: The relationship between motivational processing and outcome processing

Abstract

Digital game-based learning (DGBL) has become a viable instructional option in recent years due to its support of learning motivation. Recent studies have mostly focused on identifying motivational factors in digital games (e.g., curiosity, rules, control) that support intrinsic motivation. These findings, however, are limited in two fronts. First, they did not depict the interactive nature of the motivational processing in DGBL. Second, they excluded the outcome processing (learners’ final effort versus performance evaluation) as a possible motivation component to sustain the iterative game playing cycle. To address these problems, situated in the integrative theory of Motivation, Volition, and Performance (MVP), this study examined the relationship between motivational processing and outcome processing in an online instructional game. The study surveyed 264 undergraduate students after playing the Trade Ruler online game. Based on the data collected by ARCS-based Instructional Materials Motivational Survey (IMMS), a regression analysis revealed a significant model between motivational processing (attention, relevance, and confidence) and the outcome processing (satisfaction). The finding preliminarily suggests that both motivational processing and outcome processing need to be considered when designing DGBL. Furthermore, the finding implies a potential relationship between intrinsic motives and extrinsic rewards in DGBL.

Introduction

A digital game is a context in which players compete in attaining game objectives by following rules and principles. The playing process is voluntary and players need to overcome challenges to achieve game objectives (Suits, 1978). Gredler (1994) insisted that game playing should be fun and competitive to engage players in the game. In the context of digital game-based learning (DGBL), game playing becomes a “serious” activity that requires players to make series of decisions to attain learning objectives (Apt, 1970, Kebritchi and Hirumi, 2008), which allows learners to restore the equilibrium state of the game learning system via autonomous playing actions (Avedon & Sutton-Smith, 1971). DGBL has been widely adopted for instructional purposes in recent years as it might be capable of helping learners achieve intended learning objectives with enjoyable learning-by-playing process (Federation of American Scientists, 2006, Gee, 2003, Prensky, 2003). During the process, players acquire learning experiences supported by interactions in games and immersed in complex learning environments that are made possible in digital games (Johnson & Huang, 2008; Pannese & Carlesi, 2007).

Digital games possess characteristics of stimulating competition, challenge, and curiosity, which could motivate learners in a learning process (Arnone, 2003, Johnson and Huang, 2008, Kebritchi and Hirumi, 2008). Recent research has discussed extensively on how digital games might motivate learners to achieve intended learning and performance outcomes (Ke, 2008, Kim et al., 2009, Malone, 1981, Papastergiou, 2009, Tüzün, 2009). Findings of these studies mainly centered on intrinsic motivational factors in multimedia learning such as challenge, curiosity, control, and fantasy (Malone and Lepper, 1987, Westrom and Shaban, 1992) that drives learning behaviors with learners’ internal events (e.g., feelings of accomplishment)(van Eck, 2006). This view, however, might not be sufficient to explain the motivational processing that learners engage in digital games. Motivational processing, as suggested by the theory of Motivation, Volition, and Performance (Keller, 2008), explains the dynamic and interactive relationship between motivational components that directs the effort of learning.

Astleitner and Wiesner (2004) argued that in any iterative multimedia learning process (e.g., digital game playing), learning motivation is the outcome of complex and interactive processes among all instructional and multimedia elements. Solely considering motivational factors cannot translate the fluidity of the motivational processing into practical instructional design, to enhance learning. Garris, Ahlers, and Driskell (2002) also argued that DGBL research and design not only need to identify motivational factors, but more importantly they also need to focus on motivational processing that is dependent on iterative game events. In digital games, as learners constantly interact with various game features in cycles, it is important to understand how these iterative playing cycles might influence learning motivation, and how motivational processing could keep the playing cycle going.

The knowledge of motivational processing in DGBL can also help DGBL designers better manage learners’ cognitive load during the playing process. The multidimensional characteristics of digital games require learners’ significant cognitive investment to process environmental and social stimuli while identifying cues for motivational processing (Huang & Johnson, 2008). If managed improperly, learners could be de-motivated by the excessive demand of motivational processing (Iyengar and Lepper, 2000, Keller, 2008), which may interrupt the learning process prematurely (Ang, Zaphiris, & Mahmood, 2007).

In addition, since all digital game systems reward players’ performance (e.g., game scores), it is necessary to consider extrinsic motivation induced by external rewards or incentives when designing and evaluating DGBL (Newby & Alter, 1989). For example, learners’ intention to compete with other players or the game system, as an extrinsic incentive, must be taken into account. Only identifying intrinsic motivational factors cannot fully demonstrate DGBL’s motivational effectiveness.

In sum, only considering intrinsic motivational factors when designing or evaluating DGBL presents a systemic problem that could impede the attainment of learning outcome. That is, it falls short in providing holistic solutions to sustain learners’ motivational processing and extrinsic motivation in highly interactive and iterative game playing processes. As a result, the learning process could be terminated owing to cognitive overload (Astleitner & Wiesner, 2004). To preliminarily address this issue, this current study intends to explore the relationship between learners’ motivational processing and outcome processing grounded in the integrative theory of Motivation, Volition, and Performance (Keller, 2008). Motivational processing, in the scope of this study, refers to the Attention, Relevance, and Confidence components of the ARCS model. This process enables learners to identify achievable performance goals in the early stage of the learning process. Outcome processing refers to the Satisfaction component of the ARCS model. This processing enables learners to evaluate the equity between invested efforts and the final learning and performance outcome (Astleitner and Wiesner, 2004, Keller, 1987a, Keller, 1987b, Keller, 2008). In DGBL, outcome processing not only allows learners to evaluate the efficiency of the learning process (efforts versus outcome), but also guides learners’ motivational processing for subsequent game playing cycles (Garris et al., 2002).

The following sections will first discuss the necessity of addressing motivational design, followed by the illustration of ARCS model of motivational design. Afterward, the MVP theory will be discussed to illustrate the conceptual relationship between motivational processing and outcome processing (Keller, 2008). Then a review of recent motivational research will also be reported to demonstrate the need to carry out motivational processing studies in DGBL.