The effect of the “Prediction-observation-quiz-explanation” inquiry-based e-learning model on flow experience in green energy learning

Highlights

• This study designed a green energy learning program by using the “prediction-observation-quiz-explanation” (POQE) model.

• Increasing incremental belief of intelligence will decrease cognitive load but increase GELSE.

• Increasing cognitive load will decrease flow, while increasing GELSE will increase flow.

• Increasing flow will increase continue intention of POQE inquiry-based learning.

Abstract

There are several models of inquiry-based learning, some of which may be practiced in experimental classroom teaching, and some of which may be used in online teaching. This study proposes the “prediction-observation-quiz-explanation” (POQE) model to design a green energy generation learning program (i.e., how solar, wind, and water can produce energy) and to test how participants' cognitive-affective factors affect their interest in using this model. A total of 396 technical high school students participated in this experimental study, and 375 valid data were collected and subjected to confirmatory factor analysis with structural equation modelling. The results indicated that incremental belief of intelligence was negatively related to cognitive load, but positively related to green energy learning self-efficacy (GELSE) in practicing POQE. Cognitive load was negatively related to flow experience, while GELSE was positively related to flow experience. Finally, flow experience was positively related to the intention to continue online learning with the POQE model. The implications of this study are that e-learning designers can use this POQE model to develop more educational content for students to learn various concepts.

Introduction

During the last decades, autonomy and agency have been central to education, and they have been used indistinctly to refer to the motivation to act with initiative in one's own learning (Fay, 1996) or to refer to the ability or capacity to self-pace one's own learning (Benson, 2007). In the field of inquiry-based learning, the integration of mobile technology enables new forms of guidance (Land et al., 2011), interactivity, or immersion in technology-supported inquiries. Recently, inquiry-based learning has been increasingly suggested as a practical approach for fostering students' interest and enhancing their motivation by linking science learning in schools (Suáreza, Spechta, Prinsenb, Kalza, & Ternie, 2018).

Inquiry-based learning is often organized into inquiry phases that together form an inquiry model (Pedaste et al., 2015). The educational literature describes a variety of inquiry phases and models. For example, the 5E learning model (Bybee et al., 2006) lists five inquiry phases: Engagement, Exploration, Explanation, Elaboration, and Evaluation. An inquiry cycle proposed by White and Frederiksen (1998) also identifies five inquiry phases, but labels them as Question, Predict, Experiment, Model, and Apply. To involve fewer phases in mobile leaning, Hong, Hwang, Liu, Ho, and Chen (2014) proposed the POE (prediction-observation-explanation) inquiry learning model. However, researchers have posited that inquiry-based learning is not a prescribed, uniform linear process. The effectiveness of adapting inquiry models may vary depending on the context (Pedaste et al., 2015). After analyzing previous studies (e.g., Bybee et al., 2006; Justice et al., 2002), we found that different descriptions of inquiry models proposed by different researchers use various terminologies to label phases that are very similar. But what is important is to design an inquiry model that can be simplified to actually be conceptually independent. In educational settings, quizzes are typically used to assess students' knowledge after learning sessions; that is, quizzes can be a powerful tool to enhance the retrieval effect (Uner & Roediger, 2018), and are also a powerful way to enhance long-term meaningful learning of educationally relevant content (Blunt & Karpicke, 2014). Garcia-Sanjuan, Jurdi, Jaen, and Nacher (2018) also pointed out that quizzes are an effective learning strategy because they support retrieval practices. Based on those studies, we added one more phase to POE, the quiz phase, between observation and explanation, giving the POQE (prediction-observation-quiz-explanation) inquiry model in which students can practice inquiry reasoning processes.

To achieve learning goals in a dynamic environment, one must also learn a task model aligned to the dynamic structure of the learning tasks for the moment-by-moment control of internal cognitive processing and emotional regulation (Bhandari & Duncan, 2014; Duncan et al., 2008). In the inquiry process, students often carry out self-directed learning, for example, by doing experiments to investigate the relations for at least one set of dependent and independent variables (Wilhelm & Beishuizen, 2003). A fundamental question regarding e-learning is how to effectively present information to promote learning in an online environment. One answer to this question comes from the modality effect of the Cognitive-Affective Theory of Learning with Media (CATLM) (Moreno, 2006) or the modality principle of the cognitive/generative theory of multimedia learning (e.g., Mayer, 2005, 2009). It should be added that in the context of this study, we are focusing on learners' implicit belief of intelligence, cognitive load, green energy learning self-efficacy (GELSE), flow, and willingness to practice the POQE model in making their discoveries of green energy knowledge.

Individuals holding an entity theory of intelligence believe that intelligence levels remain (relatively) constant over a person's lifetime, regardless of their education, effort, and experience gained (Dweck, 1986, 2000, 2012). Entity theorists believe that they can learn new things (skills, knowledge), but their underlying intelligence level essentially never changes. Students with a strong entity belief believe that no matter how much time and effort they put into learning, they are bounded by their natural level of intelligence, and their intellectual ability cannot be increased through their own efforts (Dweck & Leggett, 1988). By contrast, incremental theorists believe that intelligence can be increased and cultivated over a lifetime through hard work and continued learning (Dweck & Bempechat, 1983). Students who score highly on the incremental belief of intelligence (IBI) believe that intelligence is a malleable trait that can be enhanced through learning, time, and effort (Dweck & Leggett, 1988). Fixed mindset theorists tend not to increase their level of effort in educational and work environments because they do not believe they can improve their performance (Dweck & Leggett, 1988). Incremental theorists, however, tend to acknowledge the importance of effort when approaching a learning task (Dweck, 2000, 2012). Research indicates that individuals' implicit theories can determine their perceptions of an array of different phenomena (e.g., Chiu, Hong, & Dweck, 1997). Studies related to implicit intelligence beliefs have highlighted how students' beliefs about their ability to master skills and learn course material have important implications for their learning achievement (e.g., Dai & Cromley, 2014; Shively & Ryan, 2013). To what extent the target students of this study possess IBI was explored in this study.

Cognitive load theory suggests that the acquisition of new knowledge “tends to be conscious, relatively difficult and effortful” (Sweller, 2015, p. 2). According to previous research, cognitive load in learning situations arises from three different sources. Firstly, task complexity related to learners' previous knowledge constitutes intrinsic cognitive load (ICL) as an inherent characteristic of relevant learning material (Sweller & Chandler, 1994). Secondly, the effects of inappropriate instructional presentation add to extraneous cognitive load (ECL), which is not related to relevant learning content. Thirdly, another source of cognitive load, representing germane cognitive load (GCL), arises from the process of learning itself, specified as schema acquisition and automation within the theoretical framework in terms of long-term accounts (Kalyuga, 2011).

ICL is characterized by the number of logically related information units (e.g., symbols, concepts, procedures) which learners have to process simultaneously in working memory (Sweller, Ayres, & Kalyuga, 2011). Cognitive theory of multimedia learning proposed by Clark and Mayer (2008) suggests that meaningful learning can occur when a learner is presented with words and pictures, as they can start to build a mental representation. In this way, the learner gets engaged in active learning through multimedia such as pictures, maps, charts, figures, and graphs, or dynamic media such as videos and simulations (Weng, Otanga, Weng, & Coxa, 2018). However, complexity perceptions can lower users' value (Christopher, 2000; Wirzberger, Bijarsari, & Rey, 2017) if such features become too complex for the users when performing tasks with technology-driven devices, and would thus lower the user's interaction with them (Reychav & Wu, 2016). In brief, ICL has been addressed experimentally in relation to task complexity (Beckmann, 2010), for a prior study of learning material. Therefore, if the POQE inquiry learning model includes phases in a technology-supported learning environment, it is complex and poses a challenge to students' cognitive processes, which may therefore reduce the learning value. This is the issue to be examined in this study.

Self-efficacy is the belief about one's personal capabilities to perform a task and reach one's established goals (Bandura, 1997). According to Bandura (1977, 1993), self-efficacy is believed to impact how people think, behave, and motivate themselves. According to Bandura's (1977, 1981) definition of self-efficacy, this belief is viewed as a domain-specific rather than a global construct, because people may have different degrees of self-efficacy in terms of specific academic domains rather than displaying the same extent of self-efficacy across various fields. Regarding science self-efficacy, Britner and Pajares (2006) stated that “[Students] who have a strong belief that they can succeed in science tasks and activities will be more likely to select such tasks and activities, work hard to complete them successfully, persevere in the face of difficulty, and be guided by physiological indexes that promote confidence as they meet obstacles” (p. 486). Academic self-efficacy reflects a learner's perceived competence regarding tasks in a given academic domain (Komarraju & Nadler, 2013). For example, self-efficacy has received much attention in science learning (e.g., Lin, Liang, & Tsai, 2015a, b; Tsai Ho, Liang, & Lin, 2011). In this study, the more specific term, green energy learning self-efficacy (GELSE), was adopted.

Self-efficacy refers to the belief a person has in regard to his or her ability to execute specific actions relative to the achievement of specific outcomes (Feltz, 2007, p. 278). In the learning process, Schmidt and Braun (2006) argue that any learning support devices should consider self-efficacy in the awareness of self-paced learning. In addition, when performing a learning activity, the learner's psychological state needs to be investigated in an effort to achieve a better understanding of the potential offered by learning devices (Rogers, Connelly, Hazlewood, & Tedesco, 2010). Thus, the provision of the POQE inquiry learning model that positively impacts a learner's perceptions of GELSE would be expected to ultimately have an impact on his/her green energy learning performance. There is thus a need to establish a greater awareness of learners' GELSE when facing problems in the use of POQE devices.

Flow is defined as a state of optimal experience in which a person derives pleasure from focusing on a task, regardless of extrinsic rewards (Csikszentmihalyi, 1990). Flow emphasizes the positive aspects of learning. People can experience enjoyable moments when they are immersed in a task or an activity (Tobert & Moneta, 2013). Basically, when individuals are involved in a flow state, their attention is attracted by activities and goals, and they may not recognize the tools required to create the experience (Vittersø, Vorkinn, & Vistad, 2001). Csikszentmihalyi (1990, 1998) stated that flow effects are mainly based on individuals' internal perceptions, but many existing studies have focused on the effects of learning flow during web-based instruction or computer-based learning (Kim & Kim, 2005; Kuhnle & Sinclair, 2011). Another study on e-learning flow and performance has demonstrated that flow is positively correlated with academic performance (Kiili, de Freitas, Arnab, & Lainema, 2012). Flow occurs when an individual feels that a given task is challenging and that s/he has a high level of skill with which to meet the challenge (Liao, 2006; Moneta, 2004). Therefore, multimedia learning can effectively enhance learners' flow levels.

Flow is experienced in a variety of activities such as playing sports (Wanner, Ladouceur, Auclair, & Vitaro, 2006), reading a book, or social media (Peleta, Ettisb, & Cowart, 2017). In activities within a computer-mediated environment, Hoffman and Novak (2009) stated that online flow can be experienced when one is completely immersed in an online activity. Kiili et al. (2012) found that simulation games could enhance university students' flow, especially for a sense of control, clear goals, rewarding experiences, and feedback. These experiences help learners feel concentrated. As for challenging tasks in e-learning, if they are too simple, people would easily get bored; however, if they are too difficult, they would feel frustrated and disappointed. Thus, how the POQE inquiry learning model can generate students' flow experience was of interest in this study.

There are various studies in the literature of this area which emphasize that students' active and effective participation in designed online courses is essential for the success of these learning environments (e.g., Bourelle, Bourelle, Knutson, & Spong, 2016; Hranstinski, 2009; Mandernach, Gonzales, & Garnett, 2006; Masters & Oberprieler, 2004). Some researchers such as Davis (1989) and Venkatesh, Morris, Davis, and Davis (2003) have for many years conducted studies and developed theories about the acceptance, adoption, and utilization of technological innovations. Some recent studies have found that in varied learning environments, the main focus should be on the continuance usage behavior rather than on short-term usage (Bhattacherjee, 2001; Bhattacherjee, Perols, & Sanford, 2008). Accordingly, Tsai, Lin, Hong, and Tai (2018) in their study on the continuance usage of internet-based learning, integrated a variable of learning interest into Bhattacherjee's intention model (2001). There is a lack of research examining the underlying POQE model that transforms flow into an intention to continue with POQE learning (hereafter, continuance intention). Therefore, this study aimed to examine the POQE specifically in relation to green energy inquiry learning.