Abstract
Students are commonly in a high cognitive load state when they encounter sophisticated knowledge. Whether the novel augmented reality (AR) technology can be utilized in an online learning course to explain complicated scientific concepts in a more understandable manner to students during the COVID-19 period is an unaddressed issue. This study aims to investigate the influences of reducing the physical touch or face-to-face teaching/learning practices via using mobile augmented reality learning systems (MARLS) on students’ perceived learning effectiveness. The information feedback viewpoint, flow theory, and cognitive load theory are integrated to examine the effects of the information feedback of MARLS on students’ learning effectiveness. This study recruited 204 participants from ten universities to complete a learning task via a MARLS and fill out a questionnaire to collect data for the proposed research model. The empirical results revealed information feedback positively and significantly affected flow experience, perceived learning effectiveness, and continued intention. The improved learning performance of learners was positively related to their continued intention. Also, the extraneous cognitive load negatively and significantly moderated the relationship between information feedback and perceived learning effectiveness. This study proposes meaningful implications and suggestions for future research based on the findings of this experiment.
Similar content being viewed by others
Avoid common mistakes on your manuscript.
1 Introduction
Using digital technologies, such as augmented reality (AR), has covered a wide field of instructional and learning approaches during COVID-19. AR is a frequently utilized tool via simulating or authentic scenarios to combine complex conceptions and vague images to clarify teaching/learning materials. AR allows learners to follow the information feedback to complete their learning activities supported by the interactive formats of visual or tactile related information to achieve the learning objectives. Information feedback is defined as the already evaluated/corrected and provided information transmitted to learners based on their learning processes and outcomes by AR applications (Liu et al., 2021). AR-based learning is ubiquitous in various digital applications, including photographs, games, and other means, enhancing the effectiveness of the learning experience (e.g., Bressler et al., 2013; Demitriadou et al., 2020; Ibáñez et al., 2014; Lai et al., 2019; Shin, 2019). The users do not necessarily possess knowledge of in-depth and broad digital technologies. Generally, AR-based learning applications often assist learners to obtain knowledge by offering them reliable guidance or materials (e.g., information feedback), which is relatively more interesting and diverse than the traditional teaching approach (Bressler et al., 2021; Chang et al., 2022).
The AR-based learning applications may help learners understand abstract concepts and reduce their misperception of the learning materials and extraneous cognitive overload through offering high-quality information feedback (Nikou et al., 2022). Extraneous cognitive load refers to how information is presented to an individual, which can result in the need for the individual to devote additional efforts to learning it (Liao et al., 2019; Sweller, 1994). Research indicates that more studies are needed to further investigate whether learning applications supported by different technologies (e.g., AR, three-dimensional (3D), fingertip-movement-based interactions, virtual/computing object) or interactive approaches can enhance learners’ learning outcomes because most AR-supported virtual or real-world interactive learning activities rarely provide users with timely and informative feedback when they make mistakes in order to facilitate effective learning (Alhonkoski et al., 2021; AlNajdi et al., 2020; Wang et al., 2022; Westerfield et al., 2015; Yu et al., 2019). Faqih and Jaradat (2021) also state that more AR-related studies regarding user/learner adoption of AR applications are needed because AR applications enable students to think more creatively to understand learning content after the Covid-19 pandemic. Nevertheless, studies that investigate this research issue based on the information feedback perspective are scarce. Additionally, AR-based learning processes may involve the interactions among the use of various information feedback, the formation of human cognition, and the resulting learning effectiveness in an immersing learning situation based on a flow experience perspective, while the research findings may not always generate a positive learning performance or experience in the literature of other research contexts (e.g.,Karelaia & Hogarth, 2008; Lerch & Harter, 2001; Lin & Wang, 2021). Flow experience refers to an individual psychological or motivational state of complete focused attention, sense of control, and enjoyment of a given learning task, facilitating their cognitive capacities (e.g., Csíkszentmihályi, 1990; Hohnemann et al., 2022). Regarding the learners’ cognitive capacity, learners who need to accomplish both visual and tactile tasks may need to put additional efforts into the learning process, which may negative affect their learning effectiveness if the tasks were not appropriately designed.
Based on cognitive load theory, AR-based learning must give adequate feedforward that may reduce the formation and increase extraneous cognitive loads when an individual attempts a task. Therefore, the level of learners’ perceived learning effectiveness is generally high when they sense the learning tasks include an appropriate level of challenges (Chang, 2018; Hamari et al., 2016). Perceived learning effectiveness refers to the learning results of learners regarding the formative and summative evaluations. Accordingly, extraneous cognitive load may increase the capacity of individuals working memories (i.e., knowledge, skills, and abilities) when the work/learning processes involve high levels of task complexity. Individuals’ high level of working memories might inhibit their work/learning effectiveness (Martin et al., 2020; Song & Sparks, 2019; Sweller et al., 1998).
In this study, mobile augmented reality learning systems (MARLS) are referred to as mobile-based learning applications that include designing text, video content, and fingertip-to-contact screen to rotate the 3D graphics and can thus enable learners to understand the knowledge of computer motherboard architecture. Therefore, learners’ use of MARLS are expected to lead to favorable learning outcomes. Although MARLS mainly convey basic concepts, the learning content and materials delivered through them may be inherently complex. MARLS with 3D graphics are appropriate for instructors or learners to conveniently understand sophisticated knowledge and develop specific expertise. Therefore, evaluating the success of MARLS is highly dependent on learners’ continued intention to use MARLS and their perceived learning effectiveness. However, technologically enhanced learning (i.e., AR-based learning) is not always effective in terms of delivering knowledge to learners from the perspectives of information feedback and flow theory in different learning contexts (Cruz & Uresti, 2017; Guo & Ro, 2008). There is still a lot of ambiguity (e.g., interactive mode and degree of fidelity) in the findings of prior studies regarding the usefulness of MARLS, and there is a need for more studies on such applications (Maier et al., 2016, Jiu et al, 2022). Additionally, adequate instructional design can reduce the formation of the extraneous cognitive load of learners, which tends to unnecessarily consume the learners’ cognitive resources (Sweller et al., 1998). Thus, this study evaluated the MARLS to practice the basic concepts of computer motherboard architecture. MARLS offer a novel and 3D learning space through visual and tactile interaction with the learning materials to enhance learner flow experiences. In this study, task-oriented learning materials could be accessible via the navigation hyperlinks, and the MARLS structure is thus based on the AR application. MARLS can provide dynamic visual and tactile guidance by information feedback to aid learners’ understanding and higher-order thinking in achieving predictable perceived learning effectiveness (Chiang et al., 2022; Ebadi & Ashrafabadi, 2022). Nevertheless, studies that evaluate the effectiveness of the use of MARLS for supporting student learning in higher educational contexts by adopting a comprehensive perspective that incorporate the concepts of information feedback, flow experience, and cognitive load theory are missing from the existing literature.
Therefore, this study aims to fill this gap by focusing on investigating the effectiveness of the use of the MARLS that are developed to help students learn via mobile devices (i.e., smart phones, tablets, or other mobile computing devices). To be specific, the investigation of this study is rooted in the interaction between MARLS, learning materials, and the learner. Such interaction determines learners’ perception of whether the MARLS are an effective learning tool. Consequently, we propose that the learning content and environment can achieve a good integration to enhance the learning process and increase the benefits of using MARLS in higher education settings. Therefore, our research questions (RQs) are as follows:
-
RQ: 1) Does information feedback of MARLS significantly influence learners’ flow experience, perceived learning effectiveness, and continued intention to use MARLS?
-
2) Does the extraneous cognitive load significantly moderate the relationship between information feedback and perceived learning effectiveness?
To answer those two primary research questions, this study developed the MARLS using the AR-based technologies to assist college students in learning important knowledge related to the computer hardware architecture. A total of 204 students that were recruited from ten universities and had no experience in learning the subjects covered by the MARLS of this study served as the research participants. Those students were asked to take a pre-test first, and then to use the MARLS. After completing the learning processes of the MARLS, they were asked to take a post-test and then fill out a survey in order to offering sufficient and valid data that could be used to evaluate their perception regarding the MARLS and their learning effectiveness. Details of the research design, including the sampling method, specification of the MARLS, and the data collection procedures, are presented in the subsequent sections.
2 Literature review and hypotheses development
2.1 Information feedback
Information feedback is critical to smooth task learning and perceiving a sense of interaction in the knowledge acquisition process when learners use MARLS (Burns et al., 2021; Fang, 2020). MARLS are specific instruction-based learning systems that offer knowledge-related information to learners. Thus, the information feedback of learning materials from MARLS regarding computer motherboard architecture is reliable, stable, and makes sense. MARLS may assist learners in evaluating their flow experience or observing extraneous cognitive load when they obtain high-quality information feedback (Zou et al., 2014; Ebadi & Ashrafabadi, 2022; Lin & Chen, 2020; Mystakidis et al., 2022; Wu, 2019). This is because individuals can perceive a sense of control exceeding their experiences. In summary, it can be inferred that individual flow experience plays an important mediating role in the relationship between information feedback and learning effectiveness, which is rarely examined in educational settings in which mobile AR applications are applied. Therefore, this study, by using an experimental research design, intends to specifically address this research gap in the literature.
In this study, the flow experience is considered to be composed of three main categories (Goh & Yang, 2021). First, focused attention refers to the degree to which individuals immerse and concentrate their attention on the visual screen and execute an action by forgetting everything around them. Second, a sense of control refers to an individual’s control exceeding activity requirements without conscious effort. Third, enjoyment refers to how individuals assess a particular feeling of well-being due to cognitive and affective evaluations of a specific activity. We propose the three elements of a flow state can be applied to learning activities through the learning content of MARLS because learners’ flow state of MARLS during the learning process is positively associated with their perceived learning effectiveness and continued intention (AlNajdi et al., 2020; Ibáñez et al., 2014).
2.3 Cognitive load theory
Cognitive load theory is one of the famous theoretical perspectives that concerns the relationship between cognition and educational instructions in educational psychology (e.g., Cheng, 2017; Leppink-Heuvel & van den Heuvel, 2015; Liao et al., 2019; Paas et al., 2003; Skulmowski & Xu, 2022). It has been adopted in research dealing with the design of multimedia learning materials. Learning is tending conscious; thus, the learning process is complicated and full of effort (Sweller, 1994). This learning process may be considered cognitive. Similarly, it heavily relies on a limited amount of working memory and relevant data from vast amounts of information in the long-term memory. Learners’ possession of critical knowledge and information feedback in advance (i.e., the form of information presented in 3D) can decrease their extraneous cognitive load in learning contexts (Lai et al., 2019; Moreno & Mayer, 2007; Sweller et al., 1998). Thus, for video-based social media platforms (e.g., MARLS), the visual content that delivers the learning materials must match the tactile operations of the learners to decrease their cognitive load. Additionally, the learning task complexity relates to individuals’ previous knowledge constituting their cognitive load (Sweller, 1994). The use of an inappropriate instructional method for teaching or learning may increase the extraneous cognitive load of learners. In this study, MARLS were considered a learning system that integrates learning materials and the virtual environment and links what individuals learn in real-world settings to their prior knowledge. The provision of adequate information feedback to learners in MARLS-supported educational contexts can strengthen learners’ concentration, cognition, and reflective processes.
2.4 Hypothesis development
Based on the information feedback, flow, and cognitive load theories, this study proposes the learners’ psychological states of continued intention to use MARLS and perceived learning effectiveness are influenced by their flow experience and information feedback in technology-enhanced learning. Following the fundamental notion of flow theory, this study considers the MARLS attributes of information feedback, visual and tactile information, and learning content as evaluation criteria among learners to examine the variables as aforementioned (Fig. 1).
Feedback is a resource (Bakker, 2005). The potential learning experience is weakened if the learner does not receive clear goals or explicit information regarding completion, the amount, or timing of the content (Maier et al., 2016). Researchers indicated immediate feedback (i.e., feedforward) is one of the preconditions of the flow state (e.g., Buil et al., 2018, 2019). Useful information feedback is a required component of favorable flow experience derived from individual tasks (Csíkszentmihályi, 1990). Steele and Fullagar (2009) found a significant relationship between performance feedback and flow exists when students engage in various academic activities. Therefore, some challenges and skills are necessary for tasks or activities, resulting in an optimal learning experience in a virtual learning environment. Similarly, information feedback (i.e., feedforward, cognitive, outcome) is meaningful and valuable for learners, improving their flow state (i.e., concentration, a sense of control, and enjoyment) (Csíkszentmihályi, 1975, 1990; Hattie & Timperley, 2007; Windasari & Lin, 2021). It can be inferred feedback is positively related to flow experience (e.g., Buil et al., 2018; Guo & Ro, 2008; Kajitani et al., 2020; Wang & Wang, 2008). Accordingly, the hypothesis is as follows.
-
H1a. Information feedback is positively associated with the flow experience.
In the virtual learning context, visual or tactile advanced learning technologies can assist students to enhance immersion and engagement (e.g., Ibáñez et al., 2014; Petersen et al., 2022; Shin, 2019). This can facilitate their engagement in and enhance their concentration on the learning tasks. Therefore, learners with experiencing a high level of flow state were more likely to identify or develop feasible solutions for problem-solving (Liu et al., 2011; Yang et al., 2019). Additionally, learners are likely to immerse themselves in learning tasks facing challenges when they achieve a flow state, thus, exhibiting better learning achievement (Hsieh et al., 2016; Sun et al., 2017; Wang & Hsu, 2014). The positive information feedback (i.e., audio-visual effects) may strengthen their confidence in obtaining future achievements (Teng, 2018). Some studies reported accurate responses or assessment feedback can significantly affect learners’ academic performance (Connolly et al., 2012; Zhao et al., 2021). Specifically, MARLS are an interactive teaching system that can provide immediate feedback and guidance for learners, influencing their cognitive processing (Müller & Wulf, 2022), behavioral engagement (Sun et al., 2019), and learning performance or perceived learning effectiveness (Alexiou et al., 2020; Eckes & Wilde, 2019; Yen & Lin, 2020). Accordingly, the hypothesis is as follows.
-
H1b. Information feedback is positively associated with perceived learning effectiveness.
Previous studies reported the flow experience positively affects perceived learning effectiveness in a computer-based instructional environment (e.g., Ebadi & Ashrafabadi, 2022; Wang & Hsu, 2014; Yen & Lin, 2020). For example, Rachmatullah et al. (2021) found a game-based environment promotes students’ flow experiences of genetics learning and thus positively influences their posttest scores. Li et al. (2021) reported enjoyment leads to optimal learning, and thus positively impact learners’ perceived learning effectiveness (Alexiou et al., 2020). Therefore, it can be considered the flow experience can appraise the benefits in a virtual learning environment. Accordingly, the hypothesis is as follows.
-
H2a. Flow experience is positively associated with perceived learning effectiveness.
In traditional instruction, learners typically must spend a lot of time and physical energy to engage in the learning content or skills. Nowadays, technology-enhanced applications give learners immediate access to learning materials. In a specific learning system, learners have less at risk when they engage in their learning tasks. In such a case, they will be more likely to browse and collect information for their learning. In this study, MARLS provide learners with an opportunity to learn and apply AR applications for obtaining focused attention, a sense of control, and pleasure. Thus, researchers may need to consider the aspects of flow experience and continued intention. This may be why learners’ willingness to continue using MARLS to improve their academic performance needs to examine their decision-making behaviors. Several studies indicated learners perceived flow experience while using a learning system or service affects their continued intention (e.g., Choi, 2022; Goh & Yang, 2021; Tuncer, 2021; Yang et al., 2014). When learners are in an optimum emotional state of the learning experience, they will have concentration, a sense of control, and enjoyment, influencing their continued intention to use MARLS (Zha et al., 2016). Accordingly, the hypothesis is as follows.
-
H2b. Flow experience is positively associated with the learner’s continued intention to use MARLS.
Currently, online learning has spread widely to various levels of learners. However, continuing to use technology-enhanced systems is still a critical issue (e.g., Liao et al., 2015; Lin et al., 2014; Wang & Lin, 2021). This study considers the actual success of MARLS to be related to learners' continued usage behaviors that can assist learners’ perceptions and improve their perceived learning effectiveness. They will be willing to use MARLS to support their learning and enhance their effectiveness. Previous studies on the continued intention to use online learning systems suggest perceived learning effectiveness significantly affects learners’ behavioral intention (Liaw & Huang, 2016; Liu et al., 2021; Tawafak et al., 2020). In this study, the purpose of MARLS is to facilitate learners’ learning of the knowledge of the computer motherboard architecture, making them feel confident to engage in learning tasks and subsequent assessments, thus promoting their intention to continue using MARLS. It can be inferred perceived learning effectiveness is associated with learners’ intention to continue using the MARLS. Accordingly, the hypothesis is as follows.
-
H3. Learners’ perceived learning effectiveness is positively associated with their continued intention to use MARLS.
The impact of AR technology has widely influenced learners’ behavioral patterns in education settings. For example, some prior studies argue a guide/guideless video or game can significantly affect a learner’s academic performance in a learning activity (e.g., Lai et al., 2019; Tawafak et al., 2020). Therefore, the design of learning technologies may involve a variety of information feedback to meet the users’ learning goals and desired academic performance success. Research has shown these technologies aim to achieve faster information feedback and flexibility for learners; similarly, they have to take on challenging learning tasks in their learning process (Wang & Lin, 2021). Previous studies argued learners’ learning performance/outcome is associated with their continued intention (Wongwatkit et al., 2020). Accordingly, the hypothesis is as follows.
-
H4. Learners’ improved learning performance is positively associated with their continued intention to use MARLS.
Previous research indicated extraneous cognitive load is negatively associated with academic performance or the effectiveness of cognitive processes (Cheng et al., 2021; Su, 2016). However, a study stated proper task demands or characteristics in a learning procedure or instructional design could alter the extraneous cognitive load of learners (Skulmowski & Rey, 2017). In this study, the learning material is not pure text-based which may generate additional cognitive load for most participants. Additionally, most of them are using MARLS for the first time in their learning tasks at a high level of thinking about the new information, and thus, the impact of extraneous cognitive load cannot be neglected (Hollender et al., 2010; Jiang et al., 2018). They need to integrate the visual and tactile sensations and follow the given information from MARLS, which may influence their cognitive load (Makransky et al., 2019; Petersen et al., 2022). Lee and Hong (2022) found cognitive load has a moderating effect on the relationship between epistemic prompting and students’ multimodal multiple text comprehension. Accordingly, the hypothesis is as follows.
-
H5. Extraneous cognitive load negatively moderates the relationship between information feedback and perceived learning effectiveness.
3 Methodology
3.1 Experiment
The experiment conducted for this study aimed to understand the impact of MARLS in learning the knowledge of computer motherboard architecture. In this study, information feedback and flow experience were examined to discover their effects on perceived learning effectiveness and to understand their continued intention on learners’ improved learning performance in the contexts of using MARLS. The design of the MARLS developed for this study was described in the subsequent sections.
The MARLS used in this study were developed to help student learn about the technical specifications of the motherboard of a personal computer and the key components of the motherboard, which is one of the learning subjects of the course of “Computer Architecture.” The research participants were able to use the MARLS using a mobile device running on the Android operating system, including smart phones and tablet computers. To avoid the potential disturbance in the surrounding environment, the research participants of this study were asked to complete the learning processes of the MARLS using a tablet computer in a lab designated by the researchers. The research participants were asked to take a pre-test and a post-test before and after using the MARLS in order to offer us sufficient data to evaluate how well their knowledge of the focal learning subjects was improved. After completing the learning processes of the MARLS, they were also asked to fill out a survey in order to offering data that could be used to evaluate their perception regarding the MARLS and their perceived learning effectiveness.
Before using the MARLS, the designed system would give the learners five questions to evaluate their prior knowledge regarding computer motherboard architecture (i.e., the pre-test). The feedback included three parts. First, regarding feedforward, MARLS presented some instructions to learners regarding how to operate and control the MARLS functions as well as the learning objectives of the MARLS. Then, the learner could begin the learning tasks and read the learning materials via the MARLS interface to gain new knowledge. The research participants could immerse themselves in the learning content through the guidance of the feedforward that included information for hel** them understand the relevant concepts of computer motherboard architecture. Second, after completing all learning content, the system showed a number of exam questions to assess the levels of the students’ learning effectiveness (i.e., the post-test). Third, MARLS automatically provided students with different cognitive feedback and outcome feedback based on the learners’ post-test scores. The former provided the relevant instructions for the post-test response, while the latter provided learners with post-test scores. To be specific, when a participant’s post-test score was 100 of out 100, the MARLS provided a message to the participant to indicate that they had understood the learning materials very well. Additionally, MARLS offered instructions (i.e., cognitive feedback) to a participant by referring to the exam questions that the participant got the answers to wrong in order to help him/her improve his/her understanding of the focal learning materials when his/her post-test scores were between 60 and 99. Finally, MARLS provided a participant who scored lower than 60 in the post-test with instructions (i.e., cognitive feedback) related to the exam questions that he/she got the answers to wrong in order to help him/her improve his/her understanding of the focal learning materials, and then asked him/her to take the post-test again (see Fig. 2). While the MARLS were designed to repeat the same process of retaking the post-test until the post-test score of a participant was 60 or above out of 100 (since the score of 60 out of 100 is the minimum score required to pass an exam in Taiwan), none of our participants was required to take the post-test for more than two times.
3.2 System overview of the MARLS used
The MARLS included three major components: (1) interactive interfaces that include fingertip videos, interactive visual functions, and information rendering overlay; (2) hardware components that include camera, interactive semantics, and interactive data for introducing the hardware components (i.e., mainframe computer motherboard architecture, central processing unit, and random access memory (RAM)); (3) communication tools that include a fingertip interactive information acquisition module and an online registration function for the authentication of user identities (Jiu et al., 2022). The reason is that when students perceive the learning processes are under their control and do not beyond their cognitive abilities because of the provision of appropriate and timely information feedback, they are likely to be interested and concentrated in the focal learning tasks and perceive their learning experience to be enjoyable.
In this study, the participants can perform their learning tasks through 3D models and interactive omnidirectional videos that may increase their learning motivation and keep their cognitive resources available for learning (Skulmowski & Xu, 2022). A worth noting advantage of 3D-based learning functions, although they are more challenging for the human brains to process, is that they can overcome the disadvantages of the learning processes supported by two-dimensional (a flat object) visual presentation, such less effective and less interesting human–computer interactions and poor virtual control ability regarding the interactive assembly instructions (Alhonkoski et al., 2021; Jiu et al., All the proposed hypotheses were supported (Fig. 4). The information feedback is positively and significantly associated with flow experience (H1a: β = 0.39, t = 6.54) and perceived learning effectiveness (H1b: β = 0.48, t = 10.23). There was a positive and significant relationship between flow experience and perceived learning effectiveness (H2a: β = 0.30, t = 5.58), and continued intention (H2b: β = 0.22, t = 3.14), respectively. Perceived learning effectiveness is positively and significantly associated with continued intention (H3: β = 0.56, t = 8.95. Improved learning performance is positively and significantly associated with continued intention (H4: β = 0.13, t = 2.32). Extraneous cognitive load negatively and significantly moderated the relationship between information feedback and perceived learning effectiveness (H5: β = -0.14, t = 2.51). In examining H1a, the finding indicated information feedback is positively related to perceived learning effectiveness, consistent with the previous studies (Buil et al., 2018; Guo & Ro, 2008; Kajitani et al., 2020; Wang & Wang, 2008). This suggests learners with more information feedback will reduce the uncertainty of the learning requirements and increase the strength of belief in the learning environment of MARLS. Particularly in this study, relative information feedback may make them engage in and do their best; thus, potentially improving their incentive to study and leading to better-perceived learning effectiveness. Similarly, the result of H2a reveals a learner with a higher flow experience is associated with perceived learning effectiveness, which is in line with previous studies (e.g., Ebadi & Ashrafabadi, 2022; Li et al., 2021; Wang & Hsu, 2014; Yen & Lin, 2020). This study suggests MARLS can enhance learners’ flow experience (i.e., focused attention, sense of control, and enjoyment), resulting in better-perceived learning effectiveness. In examining H1b, the findings indicated information feedback is positively associated with flow experience, consistent with the findings of previous studies (e.g., Alexiou et al., 2020; Alexiou et al., 2020; Eckes & Wilde, 2019; Yen & Lin, 2020). Real-time or detailed information feedback may facilitate the flow experience in the MARLS context while promoting focused attention, a sense of control, and fun in MARLS learning practices. In examining H2b and H3, the results revealed learners with a high level of flow experiences would have a higher level of continuance intention to use MARLS for their learning. H2b is consistent with previous studies (e.g., Choi, 2022; Goh & Yang, 2021; Tuncer, 2021; Yang et al., 2014) and H3 is in line with previous studies (e.g., Liaw & Huang, 2016; Liu et al., 2021; Tawafak et al., 2020). Flow experience is mainly derived from focused attention, a sense of control, and enjoyment to maintain or build high levels of positive psychological state in a learning activity. Thus, learners with a higher flow experience will promote their continuance intention positively. In the case of visual- and tactile-based learning, enjoyment and continuance intention are associated with each other. Additionally, MARLS reveal it can enable and support learner tasks, leading to a strong continued intention to use MARLS. The confirmation of H4 is in line with the result of Wongwatkit et al. (2020). This means MARLS can assist learners to improve learning performance, leading to the development of a high willingness to use this learning system. H5 is a novel finding that can provide a reference opportunity to improve the teaching/learning design. H5 reveals extraneous cognitive load significantly and negatively moderates the influences of information feedback on perceived learning effectiveness. Additionally, extraneous cognitive load is significantly negatively related to perceived learning effectiveness (β = -0.12, t = 2.55), consistent with a previous study (Chang, 2018). This study suggests the MARLS developers and course instructors need to carefully design the mechanisms for offering information feedback in the MARLS use context (i.e., learning materials and teaching practices) to diminish learners’ cognitive load (Ebadi & Ashrafabadi, 2022; Moreno & Mayer, 2007). Offering more ongoing feedback for instructions, clear visual presentations, and rearranging the sequence/range of examples might help them to improve their perceived learning effectiveness. Overall, the findings of this study have pointed out the important influences of information feedback on perceived learning effectiveness by enhancing the flow experience of MARLS users. Therefore, pedagogical methods that are supported by MARLS may offer timely and adequate information feedback to students in order to arouse their interest toward learning tasks, which will lead to more favorable flow experience and better learning outcomes of the students. First, in the measurement model, convergent validity primarily examines the proposed constructs that are well reflected by its measured items, consisting of the factor loadings, internal consistency reliability, composite reliability (CR), and average variance extracted (AVE) (Fornell & Larcker, 1981). As shown in Appendix Table 3, the values of factor loadings show all items were significantly greater than 0.7. Table 1 shows the Cronbach's alpha values ranged from 0.7 to 0.89, the CR values ranged from 0.83 to 0.93, and the AVE ranged from 0.62 to 0.81, exceeding the criteria of 0.7, 0.7, and 0.5, respectively (Hair et al., 2019). The results revealed all constructs with excellent reliability and convergent validity were obtained in this study. Additionally, discriminant validity evaluates the structural model and whether the survey constructs are empirically distinct from other constructs (Hair et al., 2019). According to the criteria (Fornell & Larcker, 1981), the AVE value of each construct should be larger than the squared inter-construct correlation coefficients between the others in the research model; similarly, no AVE value is less than 0.50. Table 1 shows the AVE of each construct was larger than the squared inter-construct correlation coefficients between each construct. Further, Henseler et al. (2015) stated the correlations’ Heterotrait-Monotrait (HTMT) ratio could be used to assess the discriminant validity of the measurement model. High HTMT values in the research model reveal the discriminant validity is debatable. They suggest the HTMT values did not exceed 0.90 for structural models with constructs, and thus, the conceptual meaning of these constructs are similar. The results indicated all HTMT values were less than 0.89, which didn’t exceed the criteria of 0.9; moreover, the HTMT values are significantly different from 1 in the bootstrap** procedure (Table 1). The results support the presence of the discriminant validity was acceptable. Finally, information feedback, flow experience, and perceived learning effectiveness formed a concept of second-order formative constructs. They were formed by the weighted sum of their first-order reflective constructs. The principal component analysis weights are better than evaluating by the factor loadings of indicators suggested by Petter et al. (2007). To meet the basic requirements for research analysis, the authors further examined the variance inflation factor (VIF) of all items to avoid excessive multicollinearity, enhancing the validity of the formative model. Table 2 indicates none of the VIFs exceeded the criteria of 3.3 (Petter et al., 2007); thus, serious multicollinearity was not present in this study. This study uses the bootstrap** procedure by partial least squares SEM to evaluate all hypotheses of the structural model. The authors use nonparametric bootstrap** with 5,000 samples and bias-corrected 95% confidence intervals to assess all hypotheses of the structural model. Additionally, predictive relevance (Q square) can assess the structural model’s goodness of fit. According to the studies (Geisser, 1974; Stone, 1974), all endogenous constructs should be above 0. The results of the Q square values ranged from 0.02 to 0.83, which did not include 0. Additionally, Fig. 4 shows the R squared values of flow experience, perceived learning effectiveness, continued intention, and improved learning performance were 0.15, 0.49, 0.49, and 0.03, respectively. MARLS incorporate mobile devices and AR applications in the instruction/learning on computer motherboard architecture, allowing learners with a positive flow state to complete their learning tasks, improving perceived learning effectiveness and continued intention. MARLS use visual and tactile information feedback to assist learners in completing their tasks in the learning procedure. The positive flow state can help their absorption, memory, and cognitive abilities. Therefore, appropriate approaches (i.e., strategies, tools, and information feedback) are needed to facilitate learner awareness, reflection, intention, or action. This study suggests new learning material content incorporated into technologically-enhanced learning design could significantly increase perceived learning effectiveness and continued intention among learners of MARLS. Such digitized MARLS facilitate learners’ interest and motivation toward learning knowledge of a specific challenging learning area by offering them an interesting and interactive learning environment (Bressler et al., 2021). The finding of this study can provide us with important insight and valuable information for future studies and various stakeholders to use as guidelines for develo** new tools. This study proposed a technologically-enhanced learning process that strengthens learners’ perceived learning effectiveness through MARLS to offer suitable learning support by enhancing the understanding of the learning materials. The findings of this study have some implications and limitations described as follows. In the context of using MARLS in higher educational institutes, this study is among the first group of studies that integrate the theoretical views of information feedback, flow experiences, and cognitive loads to explain how MARLS can positively influence students’ perceived learning effectiveness. The validation of the causal relationships among information feedback, flow experience, perceived learning effectiveness, improved learning performance, and continued intention to use MARLS can help us better understand learners’ learning experiences and behaviors in AR-based learning environments. The results significantly contribute to the existing literature, and offer implications regarding how the learners in higher educational institutes can be motivated to use the AR-based learning applications that are similar to the MARLS. To be specific, the research results imply that when the level of the quality of the information feedback of the MARLS is high, the MARLS users can be more immersed in the learning processes (i.e., flow experience), thus resulting in better learning outcomes (Chen et al., 2021; Müller & Wulf, 2022). To elaborate on the statement above, the research results of this study contradicted some of the prior studies (e.g., Burns et al., 2021; Lerch & Harter, 2001; Lin & Wang, 2021; Yen & Lin, 2020) and indicate that information feedback are helpful for supporting learning, as found in some other prior studies (e.g., Kajitani et al., 2020; Eckes & Wilde, 2019; Rodríguez et al., 2022). Those results thus contribute to the theory development of future MARLS studies by confirming the significant positive effects of information feedback on students learning effectiveness in MARLS contexts, and highlight the importance of including the concept of information feedback in future studies that specifically examine the effectiveness of using MARLS to support student learning in various educational contexts, which, to the best of our knowledge, has not been done in prior studies of MARLS. Additionally, our findings reveal that when the MARLS are easily accessible and provide learners with timely and useful feedback, the learners are more likely to develop a more thorough understanding of the learning subjects. This is because the learners can feel more motivated to learn, be more immersed in the learning tasks, and find the learning processes to be more enjoyable (i.e., flow experience), thus resulting in better learning outcomes (Chen et al., 2021; Müller & Wulf, 2022). Therefore, MARLS users can be benefited by an AR-supported student-centered pedagogical method that can eliminate the obstacles of traditional learning, such as external distractions in the learning environment and less enjoyable learning processes, via the formation of flow experience (Okai-Ugbaje et al., 2022). Finally, the significant direct and moderating effects of extraneous cognitive loads indicate educators cannot avoid the negative influence of extraneous cognitive loads on the learning process. While digital learning materials and teaching approaches have widely spread to various educational settings nowadays, extraneous cognitive loads' direct and intervening effects cannot be neglected. The extraneous cognitive loads are an obstacle to effective learning, and educators should devote themselves to eliminate the learning materials that may result in increases in learners’ extraneous cognitive loads when develo** their teaching content and tools. This study also has practical implications for instructors, students, and other stakeholders. First, regarding the intervention of extraneous cognitive load, this study suggests researchers and instructors should design and build a clear relationship between the figures, diagrams, and text. In such a case, it may reduce learners' cognitive load and be helpful for them to build learning concepts, facilitating their perceived learning effectiveness. Second, from the viewpoint of information feedback, MARLS can effectively support instructors’ teaching by offering prompt information feedback, in various kinds of format, that may be used as guidelines for learning and eventually contribute to the enhancement of the learners’ learning effectiveness. To elaborate on this, using the functions of MARLS helps instructors to monitor the learning progress of students by referring to the MARLS feedback. In such a learning situation, learners can move forward or backward to understand the episode of learning content to deepen their impression. Additionally, instructors may align appropriate teaching approaches (e.g., situational teaching) with the use of information feedback in a MARLS-based educational context. In other words, MARLS are capable of presenting learning instructions (i.e., information feedback) at any stage of the learning processes. Therefore, instructors can thus develop useful feedback and offer it to the learners at the appropriate points of time based on the learners’ progress in the MARLS learning processes in order to ensure the learners achieve the focal learning objectives in an effective manner. For example, instructors may use automatic message generation functions that can automatically generate useful information feedback (e.g., feedforward and cognitive feedback) at the appropriate points of time during the MARLS learning processes in order to help students reflect on what is learned and/or concentrate on what is to be learned (Rodríguez et al., 2022), thus enhancing their flow experience and learning effectiveness. Finally, regarding flow experience, a learner’s engagement or immersion in MARLS can symbolize a flow state (Bressler & Bodzin, 2013; Tang et al., 2022). If learners perceive learning tasks involving meaningful experiences, they may maintain a sense of control and pay more focused attention to learning goals. Flow experience promotes learners' perceived learning effectiveness or performance (i.e., decreased technological frustrations, made information or navigation easier by vision-based AR, and enjoyed having to think) and continued intention to use MARLS. In summary, this study revealed the important role that learners’ flow experience plays in facilitating learners’ learning effectiveness in MARLS-supported educational contexts. Therefore, future MARLS developers should carefully consider how to design the MARLS that adequately align the learning processes with the primary learning objectives, which can eliminate users’ learning pressure in order to facilitate the formation of their flow experience and improve their learning outcome (Chang et al., 2022; Kajitani et al., 2020; Skulmowski & Xu, 2022; Yu et al., 2019). Some limitations exist in this study. The learning material content only focuses on computer motherboard architecture. The results of this experiment suggest future studies could improve learning material content design by incorporating more conceptual diagrams, tables, and quizzes into their experiment planning regarding IT-related or other science curricula. Additionally, the participants only focused on the first-time use of MARLS and the conception of computer motherboard architecture; thus, the technical and practical conceptions are simple. Future study can increase the difficulty in the curriculum with more depth and breadth of materials to strengthen the learners’ abilities. Moreover, undergraduate students or above were invited to participate in this study. Concerning the information feedback mechanism of MARLS, subsequent researchers can design the appropriate feedback based on the learners’ age, gender, or familiarity levels with computers. For example, using diverse images may stimulate positive emotional responses with complete information feedback for learners. Further, this study uses mobile devices and AR applications for this experiment. Subsequent researchers can combine diverse technologies and techniques to develop a more optimal learning system to strengthen the feasibility and development of potential limitations in various educational settings, including space, time, etc.4 Results and discussion
4.1 Hypothesis testing results and discussion
4.2 The validity of measurement model
4.3 The validity of structural model
5 Conclusion
5.1 Theoretical implications
5.2 Practical implications
5.3 Limitation
Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
References
Ahn, T., Ryu, S., & Han, I. (2007). The impact of web quality and playfulness on user acceptance of online retailing. Information & Management, 44, 263–275. https://doi.org/10.1016/j.im.2006.12.008
Alexiou, A., Schippers, M. C., Oshri, I., & Angelopoulos, S. (2020). Narrative and aesthetics as antecedents of perceived learning in serious games. Information Technology & People, 35(8), 142–161. https://doi.org/10.1108/ITP-08-2019-0435
Alhonkoski, M., Salminen, L., Pakarinen, A., & Veermans, M. (2021). 3D technology to support teaching and learning in health care education-A sco** review. International Journal of Educational Research, 105, 101699. https://doi.org/10.1016/j.ijer.2020.101699
AlNajdi, M., Alrashidi, M. Q., & Almohamadi, K. S. (2020). The effectiveness of using augmented reality (AR) on assembling and exploring educational mobile robot in pedagogical virtual machine (PVM). Interactive Learning Environments, 28(8), 964–990. https://doi.org/10.1080/10494820.2018.1552873
Bakker, A. B. (2005). Flow among music teachers and their students: The cross-over of peak experiences. Journal of Vocational Behavior, 66, 26–44. https://doi.org/10.1016/j.jvb.2003.11.001
Belda-Medina, J., & Calvo-Ferrer, J. R. (2022). Integrating augmented reality in language learning: Pre-service teachers’ digital competence and attitudes through the TPACK framework. Education and Information Technologies, 1-24. https://doi.org/10.1007/s10639-022-11123-3
Bressler, D. M., & Bodzin, A. M. (2013). A mixed methods assessment of students’ flow experiences during a mobile augmented reality science game. Journal of Computer Assisted Learning, 29(6), 505–517. https://doi.org/10.1111/jcal.12008
Bressler, D. M., Shane Tutwiler, M., & Bodzin, A. M. (2021). Promoting student flow and interest in a science learning game: a design-based research study of School Scene Investigators. Educational Technology Research and Development, 69(5), 2789–2811. https://doi.org/10.1007/s11423-021-10039-y
Brooks, C., Huang, Y., Hattie, J., Carroll, A., & Burton, R. (2019). What is my next step? School students’ perceptions of feedback. Frontiers in Education, 4, 96. https://doi.org/10.3389/feduc.2019.00096
Buil, I., Catalán, S., & Martínez, E. (2018). Exploring students’ flow experiences in business simulation games. Journal of Computer Assisted Learning, 34(2), 183–192. https://doi.org/10.1111/jcal.12237
Buil, I., Catalán, S., & Martínez, E. (2019). The influence of flow on learning outcomes: An empirical study on the use of clickers. British Journal of Educational Technology, 50(1), 428–439. https://doi.org/10.1111/bjet.12561
Burns, E. C., Martin, A. J., & Evans, P. A. (2021). The role of teacher feedback-feedforward and personal best goal setting in students’ mathematics achievement: A goal setting theory perspective. Educational Psychology, 41(7), 825–843. https://doi.org/10.1080/01443410.2019.1662889
Chang, H. Y., Binali, T., Liang, J. C., Chiou, G. L., Cheng, K. H., Lee, S. W. Y., & Tsai, C. C. (2022). Ten years of augmented reality in education: A meta-analysis of (quasi-) experimental studies to investigate the impact. Computers & Education, 104641. https://doi.org/10.1016/j.compedu.2022.104641
Chang, C. C. (2018). Outdoor ubiquitous learning or indoor CAL? Achievement and different cognitive loads of college students. Behaviour & Information Technology, 37(1), 38–49. https://doi.org/10.1080/0144929X.2017.1394366
Chang, C. C., Liang, C., Chou, P. N., & Lin, G. Y. (2017). Is game-based learning better in flow experience and various types of cognitive load than non-game-based learning? Perspective from multimedia and media richness. Computers in Human Behavior, 71, 218–227. https://doi.org/10.1016/j.chb.2017.01.031
Chen, T. L., Lai, W. C., & Yu, T. K. (2021). Participating in online museum communities: An empirical study of Taiwan’s undergraduate students. Frontiers in Psychology, 11, 565075. https://doi.org/10.3389/fpsyg.2020.56507
Cheng, K. H. (2017). Reading an augmented reality book: An exploration of learners’ cognitive load, motivation, and attitudes. Australasian Journal of Educational Technology, 33(4), 53–69. https://doi.org/10.14742/ajet.2820
Cheng, Y. P., Shen, P. D., Hung, M. L., Tsai, C. W., Lin, C. H., & Hsu, L. C. (2021). Applying online content-based knowledge awareness and team learning to develop students’ programming skills, reduce their anxiety, and regulate cognitive load in a cloud classroom. Universal Access in the Information Society, 1–16. https://doi.org/10.1007/s10209-020-00789-6
Chiang, F. K., Shang, X., & Qiao, L. (2022). Augmented reality in vocational training: A systematic review of research and applications. Computers in Human Behavior, 129, 107125. https://doi.org/10.1016/j.chb.2021.107125
Chiang, T. H., Yang, S. J., & Hwang, G. J. (2014). An augmented reality-based mobile learning system to improve students’ learning achievements and motivations in natural science inquiry activities. Journal of Educational Technology & Society, 17(4), 352–365.
Choi, E. Y. (2022). The mediating role of interaction between watching motivation and flow of sports broadcasting in multi-channel network. SAGE Open, 12(1), 21582440211068510. https://doi.org/10.1177/21582440211068513
Chrysafiadi, K., & Virvou, M. (2013). Student modeling approaches: A literature review for the last decade. Expert Systems with Applications, 40(11), 4715–4729. https://doi.org/10.1016/j.eswa.2013.02.007
Connolly, T. M., Boyle, E. A., MacArthur, E., Hainey, T., & Boyle, J. M. (2012). A systematic literature review of empirical evidence on computer games and serious games. Computers & Education, 59(2), 661–686. https://doi.org/10.1016/j.compedu.2012.03.004
Cruz, C. A., & Uresti, J. A. R. (2017). Player-centered game AI from a flow perspective: Towards a better understanding of past trends and future directions. Entertainment Computing, 20, 11–24. https://doi.org/10.1016/j.entcom.2017.02.003
Csíkszentmihályi, M. (1975). Beyond boredom and anxiety: The experience of play in work and games. Jossey-Bass.
Csíkszentmihályi, M. (1990). Flow: The psychology of optimal experience. Harper and Row.
Demitriadou, E., Stavroulia, K. E., & Lanitis, A. (2020). Comparative evaluation of virtual and augmented reality for teaching mathematics in primary education. Education and Information Technologies, 25(1), 381–401. https://doi.org/10.1007/s10639-019-09973-5
Ebadi, S., & Ashrafabadi, F. (2022). An exploration into the impact of augmented reality on EFL learners’ Reading comprehension. Education and Information Technologies, 1-21. https://doi.org/10.1007/s10639-022-11021-8
Eckes, A., & Wilde, M. (2019). Structuring experiments in biology lessons through teacher feedback. International Journal of Science Education, 41(16), 2233–2253. https://doi.org/10.1080/09500693.2019.1668578
Faber, J. M., Luyten, H., & Visscher, A. J. (2017). The effects of a digital formative assessment tool on mathematics achievement and student motivation: Results of a randomized experiment. Computers & Education, 106, 83–96. https://doi.org/10.1016/j.compedu.2016.12.001
Fang, S. (2020). Visualization of information retrieval in smart library based on virtual reality technology. Complexity, 6646673. https://doi.org/10.1155/2020/6646673
Faqih, K. M., & Jaradat, M. I. R. M. (2021). Integrating TTF and UTAUT2 theories to investigate the adoption of augmented reality technology in education: Perspective from a develo** country. Technology in Society, 67, 101787. https://doi.org/10.1016/j.techsoc.2021.101787
Fornell, C., & Larcker, D. F. (1981). Evaluating SEM with unobserved variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.2307/3151312
Geisser, S. (1974). A predictive approach to the random effect model. Biometrika, 61(1), 101–107. https://doi.org/10.1093/biomet/61.1.101
Goh, T. T., & Yang, B. (2021). The role of e-engagement and flow on the continuance with a learning management system in a blended learning environment. International Journal of Educational Technology in Higher Education, 18(1), 1–23. https://doi.org/10.1186/s41239-021-00285-8
Guo, Y., & Ro, Y. (2008). Capturing flow in the business classroom. Decision Sciences Journal of Innovative Education, 6(2), 437–462. https://doi.org/10.1111/j.1540-4609.2008.00185.x
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203
Hamari, J., Shernoff, D. J., Rowe, E., Coller, B., Asbell-Clarke, J., & Edwards, T. (2016). Challenging games help students learn: An empirical study on engagement, flow and immersion in game-based learning. Computers in Human Behavior, 54, 170–179. https://doi.org/10.1016/j.chb.2015.07.045
Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81–112. https://doi.org/10.3102/003465430298487
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
Hohnemann, C., Schweig, S., Diestel, S., & Peifer, C. (2022). How feedback shapes flow experience in cognitive tasks: The role of locus of control and conscientiousness. Personality and Individual Differences, 184, 111166. https://doi.org/10.1016/j.paid.2021.111166
Hollender, N., Hofmann, C., Deneke, M., & Schmitz, B. (2010). Integrating cognitive load theory and concepts of human-computer interaction. Computers in Human Behavior, 26(6), 1278–1288. https://doi.org/10.1016/j.chb.2010.05.031
Hsieh, Y. H., Lin, Y. C., & Hou, H. T. (2016). Exploring the role of flow experience, learning performance and potential behavior clusters in elementary students’ game-based learning. Interactive Learning Environments, 24(1), 178–193. https://doi.org/10.1080/10494820.2013.834827
Huang, C. F., Nien, W. P., & Yeh, Y. S. (2015). Learning effectiveness of applying automated music composition software in the high grades of elementary school. Computers & Education, 83, 74–89. https://doi.org/10.1016/j.compedu.2015.01.003
Ibáñez, M. B., Di Serio, Á., Villarán, D., & Kloos, C. D. (2014). Experimenting with electromagnetism using augmented reality: Impact on flow student experience and educational effectiveness. Computers & Education, 71, 1–13. https://doi.org/10.1016/j.compedu.2013.09.004
Jaszczur-Nowicki, J., Romero-Ramos, O., Rydzik, Ł., Ambroży, T., Biegajło, M., Nogal, M., ..., & Niźnikowski, T. (2021). Motor learning of complex tasks with augmented feedback: Modality-dependent effectiveness. International Journal of Environmental Research and Public Health, 18(23), 12495. https://doi.org/10.3390/ijerph182312495
Jiang, D., Kalyuga, S., & Sweller, J. (2018). The curious case of improving foreign language listening skills by reading rather than listening: An expertise reversal effect. Educational Psychology Review, 30(3), 1139–1165. https://doi.org/10.1007/s10648-017-9427-1
Jiu, Y., Jianguo, W., Yang**, W., Jianwu, D., & **aomei, L. (2022). Fingertip interactive tracking registration method for AR assembly system. Advances in Computational Intelligence, 2(2), 1–26. https://doi.org/10.1007/s43674-021-00025-5
Kajitani, S., Morimoto, K., & Suzuki, S. (2020). Information feedback in relative grading: Evidence from a field experiment. PLoS ONE, 15(4), e0231548. https://doi.org/10.1371/journal.pone.0231548
Karelaia, N., & Hogarth, R. M. (2008). Determinants of linear judgment: A meta-analysis of lens model studies. Psychological Bulletin, 134(3), 404–426. https://doi.org/10.1037/0033-2909.134.3.404
Koç, Ö., Altun, E., & Yüksel, H. G. (2022). Writing an expository text using augmented reality: Students’ performance and perceptions. Education and Information Technologies, 27(1), 845–866. https://doi.org/10.1007/s10639-021-10438-x
Lai, A. F., Chen, C. H., & Lee, G. Y. (2019). An augmented reality-based learning approach to enhancing students’ science reading performances from the perspective of the cognitive load theory. British Journal of Educational Technology, 50(1), 232–247. https://doi.org/10.1111/bjet.12716
Lee, Y. H., & Hong, H. Y. (2022). Examining Taiwanese university students’ multimodal multiple text comprehension: Individual differences and epistemic prompting. Interactive Learning Environments, 1-19. https://doi.org/10.1080/10494820.2022.2028850
Leppink-Heuvel, J., & van den Heuvel, A. (2015). The evolution of cognitive load theory and its application to medical education. Perspectives on Medical Education, 4(3), 119–127. https://doi.org/10.1007/s40037-015-0192-x
Lerch, F. J., & Harter, D. E. (2001). Cognitive support for real-time dynamic decision making. Information Systems Research, 12(1), 63–82. https://doi.org/10.1287/isre.12.1.63.9717
Li, R., Meng, Z., Tian, M., Zhang, Z., & **ao, W. (2021). Modelling Chinese EFL learners’ flow experiences in digital game-based vocabulary learning: The roles of learner and contextual factors. Computer Assisted Language Learning, 34(4), 483–505. https://doi.org/10.1080/09588221.2019.1619585
Liao, C. W., Chen, C. H., & Shih, S. J. (2019). The interactivity of video and collaboration for learning achievement, intrinsic motivation, cognitive load, and behavior patterns in a digital game-based learning environment. Computers & Education, 133, 43–55. https://doi.org/10.1016/j.compedu.2019.01.013
Liao, Y. W., Huang, Y. M., & Wang, Y. S. (2015). Factors affecting students’ continued usage intention toward business simulation games: An empirical study. Journal of Educational Computing Research, 53(2), 260–283. https://doi.org/10.1177/0735633115598751
Liaw, S. S., & Huang, H. M. (2016). Investigating learner attitudes toward e-books as learning tools: Based on the activity theory approach. Interactive Learning Environments, 24(3), 625–643. https://doi.org/10.1080/10494820.2014.915416
Lin, H. C. K., Wu, C. H., & Hsueh, Y. P. (2014). The influence of using affective tutoring system in accounting remedial instruction on learning performance and usability. Computers in Human Behavior, 41, 514–522. https://doi.org/10.1016/j.chb.2014.09.052
Lin, P., & Chen, S. (2020). Design and evaluation of a deep learning recommendation based augmented reality system for teaching programming and computational thinking. IEEE Access, 8, 45689–45699. https://doi.org/10.1109/ACCESS.2020.2977679
Lin, Y. J., & Wang, H. C. (2021). Using virtual reality to facilitate learners’ creative self-efficacy and intrinsic motivation in an EFL classroom. Education and Information Technologies, 26(4), 4487–4505. https://doi.org/10.1007/s10639-021-10472-9
Liu, C. C., Cheng, Y. B., & Huang, C. W. (2011). The effect of simulation games on the learning of computational problem solving. Computers & Education, 57(3), 1907–1918. https://doi.org/10.1016/j.compedu.2011.04.002
Liu, Y. C., Wang, W. T., & Lee, T. L. (2021). An integrated view of information feedback, game quality, and autonomous motivation for evaluating game-based learning effectiveness. Journal of Educational Computing Research, 59(1), 3–40. https://doi.org/10.1177/0735633120952044
Maier, U., Wolf, N., & Randler, C. (2016). Effects of a computer-assisted formative assessment intervention based on multiple-tier diagnostic items and different feedback types. Computers & Education, 95, 85–98. https://doi.org/10.1016/j.compedu.2015.12.002
Makransky, G., Terkildsen, T. S., & Mayer, R. E. (2019). Role of subjective and objective measures of cognitive processing during learning in explaining the spatial contiguity effect. Learning and Instruction, 61, 23–34. https://doi.org/10.1016/j.learninstruc.2018.12.001
Martin, A. J., Ginns, P., Burns, E. C., Kennett, R., & Pearson, J. (2020). Load reduction instruction in science and students’ science engagement and science achievement. Journal of Educational Psychology, 113(6), 1126–1142. https://doi.org/10.1037/edu0000552
Mohammadyari, S., & Singh, H. (2015). Understanding the effect of e-learning on individual performance: The role of digital literacy. Computers & Education, 82, 11–25. https://doi.org/10.1016/j.compedu.2014.10.025
Moreno, R., & Mayer, R. E. (2007). Interactive multimodal learning environments. Educational Psychology Review, 19(3), 309–326. https://doi.org/10.1007/s10648-007-9047-2
Müller, F. A., & Wulf, T. (2022). Blended learning environments and learning outcomes: The mediating role of flow experience. The International Journal of Management Education, 20(3), 100694. https://doi.org/10.1016/j.ijme.2022.100694
Mystakidis, S., Christopoulos, A., & Pellas, N. (2022). A systematic map** review of augmented reality applications to support STEM learning in higher education. Education and Information Technologies, 27, 1883–1927. https://doi.org/10.1007/s10639-021-10682-1
Ng, D. T. K. (2022). Online lab design for aviation engineering students in higher education: A pilot study. Interactive Learning Environments, 1-18. https://doi.org/10.1080/10494820.2022.2034888
Nikou, S. A., Perifanou, M., & Economides, A. A. (2022). Towards a teachers’ augmented reality competencies (TARC) framework. In Interactive Mobile Communication, Technologies and Learning (pp. 203–212). Springer, Cham. https://doi.org/10.1007/978-3-030-96296-8_19
Okai-Ugbaje, S., Ardzejewska, K., & Imran, A. (2022). A mobile learning framework for higher education in resource constrained environments. Education and Information Technologies, in press, 1-23. https://doi.org/10.1007/s10639-022-11094-5
Paas, F., Renkl, A., & Sweller, J. (2003). Cognitive load theory and instructional design: Recent developments. Educational Psychologist, 38(1), 1–4. https://doi.org/10.1207/S15326985EP3801_1
Petersen, G. B., Petkakis, G., & Makransky, G. (2022). A study of how immersion and interactivity drive VR learning. Computers & Education, 104429. https://doi.org/10.1016/j.compedu.2021.104429
Petter, S., Straub, D., & Rai, A. (2007). Specifying formative constructs in information systems research. MIS Quarterly, 623-656. https://doi.org/10.2307/25148814
Rachmatullah, A., Reichsman, F., Lord, T., Dorsey, C., Mott, B., Lester, J., & Wiebe, E. (2021). Modeling secondary students’ genetics learning in a game-based environment: Integrating the expectancy-value theory of achievement motivation and flow theory. Journal of Science Education and Technology, 30(4), 511–528. https://doi.org/10.1007/s10956-020-09896-8
Rodríguez, M. F., Nussbaum, M., Yunis, L., Reyes, T., Alvares, D., Joublan, J., & Navarrete, P. (2022). Using scaffolded feedforward and peer feedback to improve problem-based learning in large classes. Computers & Education, 182, 104446. https://doi.org/10.1016/j.compedu.2022.104446
Rodríguez-Ardura, I., & Meseguer-Artola, A. (2017). Flow in e-learning: What drives it and why it matters. British Journal of Educational Technology, 48(4), 899–915. https://doi.org/10.1111/bjet.12480
Shin, D. (2019). How does immersion work in augmented reality games? A user-centric view of immersion and engagement. Information, Communication & Society, 22(9), 1212–1229. https://doi.org/10.1080/1369118X.2017.1411519
Skulmowski, A., & Rey, G. D. (2017). Measuring cognitive load in embodied learning settings. Frontiers in Psychology, 8, 1191. https://doi.org/10.3389/fpsyg.2017.01191
Skulmowski, A., & Xu, K. M. (2022). Understanding cognitive load in digital and online learning: A new perspective on extraneous cognitive load. Educational Psychology Review, 34, 171–196. https://doi.org/10.1007/s10648-021-09624-7
Song, Y., & Sparks, J. R. (2019). Building a game-enhanced formative assessment to gather evidence about middle school students’ argumentation skills. Educational Technology Research and Development, 67(5), 1175–1196. https://doi.org/10.1007/s11423-018-9637-3
Steele, J. P., & Fullagar, C. J. (2009). Facilitators and outcomes of student engagement in a college setting. The Journal of Psychology, 143(1), 5–27. https://doi.org/10.3200/JRLP.143.1.5-27
Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society: Series B (methodological), 36(2), 111–133. https://doi.org/10.1111/j.2517-6161.1974.tb00994.x
Su, C. H. (2016). The effects of students’ motivation, cognitive load and learning anxiety in gamification software engineering education: A structural equation modeling study. Multimedia Tools and Applications, 75(16), 10013–10036. https://doi.org/10.1007/s11042-015-2799-7
Sun, J. C. Y., Kuo, C. Y., Hou, H. T., & Lin, Y. Y. (2017). Exploring learners’ sequential behavioral patterns, flow experience, and learning performance in an anti-phishing educational game. Educational Technology & Society, 20(1), 45–60. Retrieved 22 July 2022 from https://www.proquest.com/docview/2147743221/fulltextPDF/FE9F967156BD4A07PQ/1?accountid=12719
Sun, J. C. Y., Yu, S. J., & Chao, C. H. (2019). Effects of intelligent feedback on online learners’ engagement and cognitive load: The case of research ethics education. Educational Psychology, 39(10), 1293–1310. https://doi.org/10.1080/01443410.2018.1527291
Sweller, J. (1994). Cognitive load theory, learning difficulty, and instructional design. Learning and Instruction, 4(4), 295–312. https://doi.org/10.1016/0959-4752(94)90003-5
Sweller, J., van Merrienboer, J. J., & Paas, F. G. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10(3), 251–296. https://doi.org/10.1023/A:1022193728205
Tang, Q., Zhang, T., & Jiang, L. (2022). Influence of blended instruction on students’ learning effectiveness: The role of flow. Education and Information Technologies, in press, 1-19. https://doi.org/10.1007/s10639-022-11224-z
Tawafak, R. M., Romli, A. B., Arshah, R. B. A., & Malik, S. I. (2020). Framework design of university communication model (UCOM) to enhance continuous intentions in teaching and e-learning process. Education and Information Technologies, 25(2), 817–843. https://doi.org/10.1007/s10639-019-09984-2
Teng, C. I. (2018). Look to the future: Enhancing online gamer loyalty from the perspective of the theory of consumption values. Decision Support Systems, 114, 49–60. https://doi.org/10.1016/j.dss.2018.08.007
Tu, J. C., & Chu, K. H. (2020). Analyzing the relevance of peer relationship, learning motivation, and learning effectiveness-design students as an example. Sustainability, 12(10), 4061. https://doi.org/10.3390/su12104061
Tuncer, İ. (2021). The relationship between IT affordance, flow experience, trust, and social commerce intention: An exploration using the SOR paradigm. Technology in Society, 65, 101567. https://doi.org/10.1016/j.techsoc.2021.101567
Wang, W. T., & Lin, Y. L. (2021). The relationships among students’ personal innovativeness, compatibility, and learning performance. Educational Technology & Society, 24(2), 14–27. Retrieved 22 July 2022 from https://www.proquest.com/docview/2515016477?pq-origsite=gscholar&fromopenview=true
Wang, C. C., & Hsu, M. C. (2014). An exploratory study using inexpensive electroencephalography (EEG) to understand flow experience in computer-based instruction. Information & Management, 51(7), 912–923. https://doi.org/10.1016/j.im.2014.05.010
Wang, H. Y., & Wang, Y. S. (2008). Gender differences in the perception and acceptance of online games. British Journal of Educational Technology, 39(5), 787–806. https://doi.org/10.1111/j.1467-8535.2007.00773.x
Wang, X. M., Hu, Q. N., Hwang, G. J., & Yu, X. H. (2022). Learning with digital technology-facilitated empathy: An augmented reality approach to enhancing students’ flow experience, motivation, and achievement in a biology program. Interactive Learning Environments, 1–17,. https://doi.org/10.1080/10494820.2022.2057549
Westerfield, G., Mitrovic, A., & Billinghurst, M. (2015). Intelligent augmented reality training for motherboard assembly. International Journal of Artificial Intelligence in Education, 25(1), 157–172. https://doi.org/10.1080/10494820.2018.1552873
Windasari, N. A., & Lin, F. R. (2021). Why do people continue using fitness wearables? The effect of interactivity and gamification. SAGE Open, 11(4), 21582440211056610. https://doi.org/10.1177/21582440211056606
Wongwatkit, C., Panjaburee, P., Srisawasdi, N., & Seprum, P. (2020). Moderating effects of gender differences on the relationships between perceived learning support, intention to use, and learning performance in a personalized e-learning. Journal of Computers in Education, 7(2), 229–255. https://doi.org/10.1007/s40692-020-00154-9
Wu, M. H. (2019). The applications and effects of learning English through augmented reality: A case study of Pokémon go. Computer Assisted Language Learning, 1-35. https://doi.org/10.1080/09588221.2019.1642211
Yang, S., Wang, Y., & Wei, J. (2014). Integration and consistency between web and mobile services. Industrial Management & Data Systems, 114(8), 1246–1269. https://doi.org/10.1108/IMDS-06-2014-0167
Yang, X., Lin, L., Cheng, P. Y., Yang, X., & Ren, Y. (2019). Which EEG feedback works better for creativity performance in immersive virtual reality: The reminder or encouraging feedback? Computers in Human Behavior, 99, 345–351. https://doi.org/10.1016/j.chb.2019.06.002
Yen, W. C., & Lin, H. H. (2020). Investigating the effect of flow experience on learning performance and entrepreneurial self-efficacy in a business simulation systems context. Interactive Learning Environments, 1-16. https://doi.org/10.1080/10494820.2020.1734624
Yilmaz, R. M., Topu, F. B., & Takkaç Tulgar, A. (2022). An examination of vocabulary learning and retention levels of pre-school children using augmented reality technology in English language learning. Education and Information Technologies, 1-29. https://doi.org/10.1007/s10639-022-10916-w
Yu, S. J., Sun, J. C. Y., & Chen, O. T. C. (2019). Effect of AR-based online wearable guides on university students’ situational interest and learning performance. Universal Access in the Information Society, 18(2), 287–299. https://doi.org/10.1007/s10209-017-0591-3
Zha, X., Zhang, J., Li, L., & Yang, H. (2016). Exploring the adoption of digital libraries in the mobile context: The effect of psychological factors and mobile context factors. Information Development, 32(4), 1155–1167. https://doi.org/10.1177/0266666915593331
Zhao, H., Liu, X., & Qi, C. (2021). “Want to learn” and “can learn”: Influence of academic passion on college students’ academic engagement. Frontiers in Psychology, 12, 2370. https://doi.org/10.3389/fpsyg.2021.697822
Zou, D., Zhang, R., **e, H., & Wang, F. L. (2021). Digital game-based learning of information literacy: Effects of gameplay modes on university students’ learning performance, motivation, self-efficacy and flow experiences. Australasian Journal of Educational Technology, 37(2), 152–170. https://doi.org/10.14742/ajet.6682
Acknowledgements
This research was supported by the Ministry of Science and Technology [grant number: XXX].
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
None
Additional information
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendix
Appendix
See Table 3
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Wang, WT., Lin, YL. & Lu, HE. Exploring the effect of improved learning performance: A mobile augmented reality learning system. Educ Inf Technol 28, 7509–7541 (2023). https://doi.org/10.1007/s10639-022-11487-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10639-022-11487-6