Keywords
Interactive learning system, augmented reality, learning experience, learning performance
This article is included in the Research Synergy Foundation gateway.
Interactive learning system, augmented reality, learning experience, learning performance
In traditional classroom environments, there exists a problem with student participation.1 It is also stated that the traditional method is inefficient as it is mostly spoon-feeding, and students’ analytical processes are absent due to a lack of peer interaction.2 Although some studies have addressed the issues of student interaction with the system there is still a lack of teacher-student or student-student interaction in interactive learning systems.1–3 Many systems have promoted a single type of interactivity.4–10
Calculus is one of the core subjects in computer science studies. Malaysian students pursuing diplomas and degrees in Information Technology take calculus subjects that include differentiation, and integration. Application of integration is one of the chapters in Calculus that requires spatial visualization and the 2D medium of the traditional classroom does not provide a proper solution.11–13
In understanding the revolution of solids around an axis, visualization of the solid in-question is very important. Spatially related 3D images cannot be drawn on a 2D board or projected over a computer. These hinder students in visualizing the solid and result in difficulties in conceptualizing the concept. Although researchers have implemented AR to assist in visualization, they are mainly focused on human-system interaction, either through haptic or non-haptic interaction.14–17 Besides, agility is another issue that some systems could not address as they are desktop-based systems.17–19 As such, developing an interactive AR application that facilitates both human-human and human-system interactivities and assists in conceptualizing and understanding math better is needed.
The use of interactive technology in pedagogy has become very common in the last decade. Interactive technology has made learning more personalized and kept students engaged through various applications.20 Abykanova et al. also suggested that interactive technology can make learning more active, and intensive for students where students can communicate easily with each-others and also with the teacher.20 Game-based learning, multimedia lectures and labs, and electronic study guides are some examples of the most common interactive technologies in the pedagogy sector. Recently implementation of Augmented Reality (AR) in pedagogy has been adopted by many researchers where the emphasis has mainly been kept on 3D visualization. Many have implemented AR in geometry for school students14,19,21 while some others implemented it for calculus.12,13 In both cases, the authors have focused on the immersive learning experience through AR that allows students to comprehend and conceptualize learning content.
Implementation of interactive learning systems in class to engage students in learning has become a common focus in the pedagogical arena. A system that promotes interpersonal interaction and provides a sense of others’ presence can be considered as an interactive system.4
In an attempt to increase class interactivity different types of technologies have been implemented in class. These can be categorized as synchronous and asynchronous mediums of interaction.22 Synchronous is used as a medium of interaction during class, especially when participating in in-class discussions, whereas asynchronous is for facilitating learning remotely, which may happen after the class. Examples of synchronous learning tools include interactive whiteboards2 and virtual-reality-based systems for learning language.4 An example of an interactive learning system for asynchronous learning is an AR-based interactive book.5 On the other hand, interactivity can also be categorized as human-system and human-human, as technologies are allowing both in a learning environment.3 Human-human interactivity is further divided into teacher-student and student-student interactivity. Table 1 shows promoted interactivities by different studies. These studies focused primarily on human-system interactivity while a few provided human-human interactivities.
No | Study | Teacher-Student | Student-Student | Interactive system |
---|---|---|---|---|
1 | 1 | ✓ | ✓ | × |
2 | 2 | ✓ | ✓ | ✓ |
3 | 4 | × | × | ✓ |
4 | 10 | × | × | ✓ |
5 | 5 | × | × | ✓ |
6 | 6 | × | × | ✓ |
7 | 43 | × | ✓ | ✓ |
8 | 9 | × | × | ✓ |
9 | 3 | ✓ | ✓ | ✓ |
10 | 7 | × | × | ✓ |
11 | 40 | ✓ | × | ✓ |
12 | 8 | × | × | ✓ |
AR has become a trending technology in the pedagogical sector to provide students with an immersive experience. AR provides a real and virtual experience together to allow users real-time interaction.16 3D representation of a virtual object in a real environment can make learning more engaging as it provides a visual learning experience. This can be helpful to the majority of students as studies have found that there are more visual learners compared to auditory or kinesthetic learners.23 Besides, haptic interaction happens when users are allowed to interact with the 3D object through AR technology24 which also address kinesthetic learners as they learn through physical involvement. Table 2 depicts the interactivities promoted by different AR studies.
No | Study | Teacher-Student | Student-Student | Interactive book/system | Mobility |
---|---|---|---|---|---|
1 | 16 | × | × | ✓ | ✓ |
2 | 24 | × | × | ✓ | ✓ |
3 | 14 | × | × | ✓ | ✓ |
4 | 18 | × | × | ✓ | × |
5 | 19 | × | × | ✓ | × |
6 | 17 | × | × | ✓ | × |
7 | 15 | × | × | ✓ | ✓ |
8 | 41 | × | × | ✓ | ✓ |
From the studies depicted in Table 2, five papers adopted mobility in their AR system except the three that were desktop-based. This table also shows that the systems solely focused on interaction through AR and not on implementing any human-human interactivity.
The learning experience can be defined as the experience a learner goes through while learning content set by an institution.25 Including active engagement and collaboration as a part of it affects learning performances26,27 Furthermore, implementation of technology in class affects the learning experience positively.28
A pre-test and post-test quasi-experimental research design was adopted. The data was collected through a personally administered questionnaire survey. As the study was designed to be conducted using a pre-and post-test methodology, a longitudinal time horizon was adopted since the study was comparing students’ learning experience and performance before and after using the application.
Sample size calculation was done by following the 10 times rule, where the sample size is required to be 10 times larger than the maximum number of structural paths directed towards a latent variable.30 As such the minimum sample for this study is supposed to be larger than 20 as two paths are directed towards the latent variable. The sample size for this research was 59, but after eliminating missing data and straight-line answers the data was analyzed from 55 respondents. The respondents were students from a Malaysian university taking Calculus. At the end, the data was analyzed and hypotheses were accepted or rejected based on the p-values. Figure 1 illustrates the research design of this study.
This research has identified two types of interactivities, human-human and human-system. Figure 2 depicts the research framework of this study.
An interactive learning system based on AR was developed. Human-system and human-human interactivities were included as part of application functions. Human-system interaction was promoted by haptic interaction based on marker-based AR technology whereas human-human interactivity was implemented through a function of discussion platform. The framework was developed to find how these two interactivities affect the learning experience, the first dependent variable, and lastly how the learning experience incurred by the promoted interactivities affects students’ academic performance.
Human-system interaction is positively associated with students’ learning experience.
Human-human interaction is positively associated with students’ learning experience.
Improvement of learning experience improves the performance of learning
This research was conducted into three phases, in the first phase the students were asked to answer a pre-questionnaire and a quiz before using the application, in the second phase they used the application and explored AR, and lastly, in the third phase, they answered a post-test questionnaire.
Figure 3 shows students exploring the AR function of the application.
The survey questionnaire was divided into segments of three. The first section (A) was demographic questions; the second section (B) evaluated the two independent variables and one dependent variable, and in the third section (C) dependent factor learning performance was evaluated through a quiz developed by the subject expert.
Section A comprises of six questions regarding participants’ general information that included gender, ethnicity, age and whether they have used any educational AR application before or not. All the questions in this section were self-designed.
Section B evaluates the variables of the research framework comprised of self-developed and adopted questions from multiple sources. Independent variable human-human interaction measurement questions were self-developed. Human-human interaction questions were formed based on the idea of peer-peer interaction and student-teacher interaction shown in Table 3.
For the second independent variable human-system interaction the questions were adapted from Sumadio and Rambli30 and are depicted in Table 4.
The learning experience variable was evaluated based on the adopted questionnaire from31 shown in Table 5 and also from the self-developed questionnaire listed in Table 6.
Section C was developed by a calculus subject expert in evaluating learning performance via a quiz. The quiz included True/False and formative questions.
This study conducted two types of analysis through Smart PLS 3.0 and SPSS 22 (IBM SPSS Statistics, RRID: SCR_019096) for conducting structural model assessment and pre-and post-test comparison of variables respectively. R is an open-source alternative software (R Project for Statistical Computing, RRID: SCR_001905) for Smart PLS.
Demographic profile
This study collected 55 valid responses from the selected sample of 59 students from a Malaysian private university (Table 7). Out of 55 respondents, 46 were male (83.6%) while 9 were female constituting 16.4% of total respondents. The result is in line with other research findings as the gender ratio significantly skews towards males in the technology field.32,33 Among the three main ethnicity groups 43 students were Chinese (78.2%), followed by six Malay and six Indians constituting 10.9% each. As the sample was from pre-university students, the age range was from 18-22. The majority of students were 19 years old (60%) where 18 and 22 were the smallest age groups, constituting 3.6 % each.
Criterion | Frequency | Percentage | |
---|---|---|---|
Gender | Male | 46 | 83.6 |
Female | 9 | 16.4 | |
Race | Malay | 6 | 10.9 |
Indian | 6 | 10.9 | |
Chinese | 43 | 78.2 | |
Age (years) | 18 | 2 | 3.6 |
19 | 33 | 60.0 | |
20 | 12 | 21.8 | |
21 | 6 | 10.9 | |
22 | 2 | 3.6 |
Students’ experience of using similar types of applications in learning provides an indication of whether users are experienced in using this type of application or not. It is found that none of the students had used an AR application before.
The study adopted a PLS_SEM approach to maximize the variance of the defined framework’s endogenous construct.34 Structural model analysis was used to test the hypotheses along with its predictive accuracy and effect size. All the constructs were measured with five or more indicators. As the model is reflective, the constructs are reflective too. “Internal consistency reliability”, “convergent validity” and “Discriminant Validity” are the three criteria used to assess the constructs.
Internal consistent reliability
To evaluate the same source biasedness coefficient variation, Internal consistency reliability is used to investigate the reliability of indicators that measure a latent variable. From Table 8, each construct satisfied the criteria of composite reliability (CR) of ≥ 0.7.35 As such it can be concluded that the constructs met the internal consistency reliability criteria.
Convergent validity
Convergent validity is measured by the indicator of Average Variance Extracted (AVE) and factor loading.35 AVE indicates what percentage of the variance of a construct is defined by a marker.35 The acceptable value of AVE for each construct must be ≥ 0.5. From the AVE value depicted in Table 8, all constructs satisfied the convergent validity criteria. Factor loading of each indicator ≥ 0.6 is acceptable.36
Discriminant validity
Discriminant validity is measured by the Fornell and Larcker Criterion (FLC), cross-loading comparison and HTMT technique.37 Table 9 shows that all constructs satisfied FLC criteria, where the square roots of AVEs for the reflective constructs of HHI (0.751), HSI (0.726) and LE (0.711) are larger than the correlation for all other constructs diagonally.
Human-Human Interaction | Human-System Interaction | Learning Experience | |
---|---|---|---|
Human-Human Interaction | 0.751 | ||
Human-System Interaction | 0.565 | 0.726 | |
Learning Experience | 0.714 | 0.616 | 0.711 |
From Table 10 of cross loading, all indicators load high on their own constructs compared to others. This confirms that the constructs are distinct from each other indicating discriminant validity as a result.
The HTMT mechanism of assessing discriminant validity requires the value of HTMT to be lower than 0.85 for stringent criterion and 0.9 for conservative criterion.37 From Table 11, all the constructs satisfy the above criteria confirming discriminant validity thereof.
Human-Human Interaction | Human-System Interaction | Learning Experience | |
---|---|---|---|
Human-Human Interaction | |||
Human-System Interaction | 0.646 | ||
Learning Experience | 0.817 | 0.672 |
From all the criteria of confirmatory factorial analysis, the research model is adequately fitting to be accepted. As such this designated measurement model with specified latent variables has been analyzed with SEM criteria.
Structural model assessment
Path analysis was performed to find the hypothesized relationship. The results for collinearity assessment and hypothesis are shown in Table 12.
Latent variables | VIF | Beta ß | t-value | p-value | f2 |
---|---|---|---|---|---|
Human-Human Interaction -> Learning Experience *** | 1.47 | 0.537 | 4.629 | 000 | 0.463 |
Human-System Interaction -> Learning Experience ** | 1.47 | 0.313 | 2.588 | 0.005 | 0.157 |
Both latent variables Human-Human Interaction (HHI) and Human-System Interaction (HSI) have positive effects on Learning Experience (LE). The variance inflation factor (VIF) results in Table 12 show that the lateral multicollinearity meets the criteria of being above the threshold of 0.2 and below the threshold of 5, implying collinearity was not an issue in the structural model.35
A study by35 suggested that for a one-tailed test “t-values” for a significant level of five per cent (α = 0.05) are required to be greater than 1.645. The result indicated that both exogenous constructs HHI and HSI have a “t-value” of >1.645 for a significant level of five per cent (α = 0.05). From the results, both latent variables have a positive relationship with LE, HHI being the stronger predictor than HSI.
Predictive accuracy, effect size and relevance
“Predictive accuracy” is evaluated through the “coefficient of determination, R2”. R2 values imply the predictive power of exogenous constructs over endogenous ones. From the analysis construct LE’s R2 value is found as 0.576 which means exogenous constructs substantially explain 57.6% of LE’s variance as predictive power can be considered as substantial if it is greater than 0.26.38 Although35 has stated R2 value less than 0.67 is moderate.
To evaluate the effect size of exogenous constructs Cohen’s f2 value was obtained from the model analysis.38 stated that the f2 value of 0.35 is considered a substantial effect whereas 0.15 is considered moderate. From Table 12 it can be seen that the HHI construct has a substantial effect on LE (0.463) and HSI has a moderate effect (0.157).
In addition, the Q2 value of endogenous construct LE was found at 0.269 indicating moderate “predictive relevance”.35 Besides, as the Q2 value is larger than 0, it can be concluded that HHI and HIS exogenous constructs have “predictive relevance” for the endogenous construct LE.39
Pre-test post-test
For understanding the significance of pre-and post-test performance of student paired sample t-test was carried out and the result is shown in Table 13. From Table 13 it can be concluded that there is a significant relationship between the results of the pre-and post-tests as the P<0.05.29 The post-test mean implies that students’ performance results in the post-test are higher than the pre-test one signifying a positive improvement in the performance of learning.
From the result of the hypotheses, two hypotheses, H1 and H2 were supported – the human-human factor has a larger impact than the human-system factor.
Human-human interaction through the application has a mean of 3.92 compared to human-human interaction without the application with a mean of 3.70. Human-human interaction affects the learning experience positively which is in line with results found by.1,2,3,40
Human system interactivity from AR has also affected learning experience positively in line with the related research.14,16,24,41 The learning experience mean score is 3.42 prior to application usage whereas later it turned to 3.68.
From the pre-and post-test results, the learning experience has increased the mean score of students’ performances. Learning experience, because of these interactivities, has affected performance positively similarly to results found in other studies.3,15
The role of interactivity in the learning experience has been established by many studies before, but implementing both human-human and human-system interactivities in an augmented reality application was overdue. This research had done exactly that and analyzed the effectiveness of the research framework by using PLS. From the results, it can be concluded that the model was fit to analyze the research framework with substantial predictive accuracy and a moderate effect size of the exogenous variable with a moderate relevance on endogenous variable LE. All three hypotheses are confirmed as P values for all three are at a satisfactory level. These results imply that human-human and human-system interactions positively affect learning experience and performance of learning as a result of an enhanced learning experience.
This article has obtained public disclosure approval from Multimedia University. The authors declare that there is no conflict of interest.
All the procedures performed in this study involving human participants were in adherence to the ethical policies of the University as approved by the Technology Transfer Office of Multimedia University under ethical approval number: EA0702021.
Written consent was also obtained from all individual participants involved in the study. Personal data from individuals was promised to be kept confidential and strictly restricted for use in this study only.
Zenodo: IARA LP Dataset. https://doi.org/10.5281/zenodo.574496042
This project contains the following underlying data:
Dataset IARA LP 01122021.xlsx (The file contains two sheets. The first one contains the indicators of three variables; Learning Experience, Human-Human Interaction and Human-System Interaction which were used for framework analysis. The second sheet includes the results of students' performance before and after using the learning system as pre-test and post-test).
Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).
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Competing Interests: No competing interests were disclosed.
Reviewer Expertise: My research interest is in software engineering education, software visualization, and project management.
Is the work clearly and accurately presented and does it cite the current literature?
Partly
Is the study design appropriate and is the work technically sound?
Partly
Are sufficient details of methods and analysis provided to allow replication by others?
Partly
If applicable, is the statistical analysis and its interpretation appropriate?
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Are all the source data underlying the results available to ensure full reproducibility?
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Are the conclusions drawn adequately supported by the results?
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References
1. Yi-Ming Kao G, Ruan C: Designing and evaluating a high interactive augmented reality system for programming learning. Computers in Human Behavior. 2022; 132. Publisher Full TextCompeting Interests: No competing interests were disclosed.
Reviewer Expertise: My research interest is in educational technologies, technology acceptance and information system
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