Teachers’ Perspectives on Using Augmented-Reality-Enhanced Analytics as a Measure of Student Disengagement

Abstract

There are various ways that teachers manage student disengagement levels during their class lessons, and managing disengagement can be both stressful and challenging, especially since each student is unique. Methods and techniques utilised are specific to teachers’ own experience level, subject knowledge, and teaching styles. We report on the techniques and methods teachers utilise to identify, mitigate, and measure student disengagement during class lessons; the paper presents the results of a mixed-methods, multisession study design comprising gathered qualitative and quantitative data to enable a greater understanding. Eight educators who were full-time educators with varying years of experience from three different schools, who taught or had taught English, maths, and science subjects at the primary school level, participated in this study. The study also observed that teachers used three AR applications and collected valuable feedback on their perspectives by using analytics generated by AR applications to help manage student disengagement. A postsession survey tool was used to gather the perceived importance and ranking of the techniques and methods discussed by the teachers during the previous sessions. The results showed that the majority of teachers deemed spending “Time on Tasks” and giving “Feedback/Reflections” most suited for measuring disengagement, and encouraging “Movement” and use of “Technology” emerged as the most favoured for mitigating disengagement. For utilising AR enhanced analytics in mitigating and measuring student disengagement, the data suggested a difference in perspectives based on teachers’ teaching levels, especially concerning conversations and the use of technology devices. The study did not find conclusive evidence of differences based on teachers’ teaching subjects and there was a notable distinction in building positive relationships among English teachers. This leads to the suggestion that subject-specific pedagogy might influence the perceived effectiveness of using AR-generated analytics in mitigating and measuring student disengagement.

1. Introduction

Teachers play a pivotal role in ensuring an effective delivery of lessons, not only by imparting knowledge, but also by attending to the diverse needs of students. As the educational landscape continually evolves, maintaining a consistent student engagement becomes an increasingly complex task. Ref. [1] highlighted the detrimental impacts of student disengagement, which manifests in various forms, from decreased academic performance to elevated dropout rates. One of the significant challenges educators face is the detection and remediation of student disengagement, a concern that is compounded by the varied definitions and measurements of disengagement across educational settings [2]. The use of learning analytics in educational settings allows teachers to measure and analyse students’ learning progress, lesson assessment, knowledge retention, thus giving measurable learning outcomes to assists teachers in addressing problems and challenges in teaching. Student engagement analytics derived from traditional methods such as classroom observations, and surveys, although beneficial, often fail to provide real-time data and can be influenced by subjective biases [3]. Rapid technological advancements in the 21st century have offered promising solutions to these challenges. Augmented reality (AR), as noted by [4], presents a novel avenue for enhancing classroom experiences while capturing instantaneous engagement metrics. This study contributes to the research literature in the use of AR applications as tools to capture student engagement levels in real classroom settings using novel analytics from mobile device sensors. We investigate teachers’ perception on the use of AR-enhanced analytics (AREA) as predictive and measurement instruments for student disengagement compared to conventional techniques. Grasping AREA’s effectiveness in this arena is pivotal, as it has the potential to revolutionise the strategies educators employ, ensuring a more tailored and engaging learning experience for students [5]. Measurement instruments in classrooms using AREA during lessons produce analytics in the form of a learning score, movement data, duration, and orientation of mobile devices. AREA is then processed via measures and metrics collected from various sensors in mobile devices from the AR application [6]. There are two research questions that this study answers: (1) How do teachers identify and address student disengagement during class lessons? (2) How does AR-enhanced analytics (AREA) capture and measure student engagement/disengagement levels?

2. Student Disengagement

Using AR applications in classroom learning offers more meaningful and engaging learning experiences that provide the benefits of interactivity and immersion to enhance the learning process, the cognitive and affective outcomes of the students, and the challenge of student participation [5,7]. In the evolving landscape of education, there has been an increasing interest in the use of augmented reality (AR) as a tool for enhancing student engagement. Ref. [7] posits that AR has the potential to offer meaningful and engaging learning experiences. This assertion aligns with the benefits of interactivity and immersion that AR can bring to the learning process, as explored by [5], which identified that AR could enhance students’ cognitive and affective outcomes, thus addressing challenges related to engagement. However, as with all technologies, the integration of AR in the classroom is not without its challenges. Ref. [4] highlighted concerns such as technological familiarity and the importance of pedagogical integration, emphasising that the mere presence of AR did not guarantee improved engagement. This is in line with [8]’s contention about the potential subjectivity of traditional methods of tracking engagement.

In addition, the efficacy of AR in predicting and addressing student disengagement remains a relatively uncharted domain. Ref. [9] stressed the need for more empirical studies to validate the educational benefits of AR, suggesting a potential gap in the literature. The study investigated the feasibility of using AR-generated analytics as a measure to predict student disengagement levels compared to current methods used by teachers. Teachers traditionally report on the level of student disengagement through various methods and track it from lesson to lesson through classroom observations, journals, assessments, feedback, and surveys. As student engagement is essential in the overall learning experience, it becomes necessary to determine the level of engagement of a student, often a challenge to first identify and then measure. Disengagement is both a process and an outcome. For example, student absenteeism may reflect disengagement from school, but it is also a risk factor for other disengagement indicators such as early school leaving. Contexts beyond the educational setting (that is, the family) are also an integral part of the disengagement processes of children and young people [10].

There is a vast literature available to draw upon, where student disengagement is conceptualised, described, and measured in many different ways across different disciplines. Measures and indicators as evidence of student engagement and disengagement are varied according to disciplines and can be broadly classified into (i) student self-reporting, (ii) experience sampling, (iii) teacher rating of students, (iv) interviews, and (v) observations [2]. When students are authentically engaged in meaningful and quality work, the likelihood that they will learn something new and remember what was learned increases. Improved student academic performance results from increased student engagement because students work harder to achieve desired results, and for a truly engaged learner, the joy of learning inspires persistence to achieve the desired goals even in the face of difficulty [11]. Student engagement is defined as the “time and physical energy that students spend on activities in their academic experience” [12]. Motivated and engaged students have the desired skills to work with their peers and to transfer knowledge to solve problems creatively [13]. Studies have established the link between student engagement and learning outcomes with increased academic success in higher education [14] and persistence to complete tasks (e.g., [15,16]. Utilising the Trickle-Down Engagement Model hypothesis [17] found that students’ perceptions of their teacher’s engagement were associated with their own engagement in the classroom. Teachers’ subjective experiences are “trickled down” and ultimately impact the subjective engagement, learning experiences, and performance of their students. When teachers provide the learning environment with the additional tools to become self-motivated, real engagement in learning takes place [18], and self-motivation comes from the desire to understand something of interest or from the enjoyment of learning in order to achieve personal goals rather than from any kind of reward or incentive.

3. Method

To answer the 2 research questions, a mixed-methods multisession study design was used, and we collected qualitative and quantitative data to investigate how teachers identified and mitigated student disengagement and the use of AREA to capture levels of student engagement. The process for collating teachers’ perspectives on answering the research questions included 4 main stages (Figure 1).

Stage 1: We studied teachers’ opinion on how they identified students who were disengaged. Teachers were invited to participate in small focus group sessions of 2 to 3 teachers to share their strategies to identify, mitigate, and report student disengagement in class lessons. Eight teachers (n = 8) responded to participate from 3 different schools and with varying backgrounds, expertise, and experiences. Focus-group sessions enabled the collection of their insights and experiences on various strategies and methods used to measure and mitigate disengagement in classrooms.

Stage 2: After the focus group sessions, the teachers were introduced to three AR applications related to education settings. The AR applications’ user experiences made it possible to rapidly understand the perspectives of teachers on opportunities for young students to self-engage and collaborate with one another, which allowed them to transfer the level of engagement skills. As each teacher used the AR applications, they shared their perceptions on the use of AR technologies in classroom teaching, keeping student engaged, and identifying AR-enhanced analytics implementation of strategies and methods to address disengagement. The three AR applications used in the teachers’ AR sessions are shown in Figure 2. These AR applications were designed in-house for students to experience AR in their learning process both in the classroom and outdoors. They were developed with AREA capabilities which enabled the capture and measure of the students’ user experiences from device sensors, presenting the analytics in the form of game scores, screen interactions of tapping and swiping, movement data, duration of play, and orientation of mobile devices.

(a)

Sheep Shearing AR App

This innovative AR game was developed as part of the educational awareness and discovery program to teach students about wool. In addition to learning about the quality of wool fibre, the students learned about the sheep shearing process and becoming a shearer in an AR experience. The AR application required the user to interact (tapping/swiping) the mobile device screen, to move around the virtual sheep and keep a focus on the sheep at the same time. Racing against a countdown timer to finish the game, and a total score of the percentage of wool removed from the sheep as a final result of the user experience was then presented.

(b)

Forest Adventure AR book App

The forest classroom experience used AR to support educational and motivational goals, using the story book metaphor to ensure that teachers and young children related to its purpose as a literacy development tool. It was distinctive in its focus on an age group (6 to 9 years old) in which learning is best supported by exploration and participation. Teaching abstract principles of sustainability was achieved through imaginative narrative and engaging characters that were brought to life in 3D on every page of the storybook through AR so that students could identify with the characters and their plight.

(c)

Garden of Eden AR App

The AR application transformed the user’s environment to an immersive virtual digital space filled with greenery, water, and blue sky. Through any of the “portal-doors” the user was able to transport to various locations of the Garden to explore the different environment, sound, and interactions.

Stage 3: We identified specific themes suggested by teachers from the focus group and the hands-on session with the AR apps that addresses the research questions. A student researcher triangulated the findings from stages 1 and 2 into a feedback questionnaire ranking the perceived importance of the discussed strategies and methods provided by educators in all the multiple sessions. This feedback questionnaire was developed based on the identified themes using coding techniques from the focus group’s recorded interviews and notes.

Stage 4: We designed a feedback questionnaire from the coded themes and sent it to a wider group of teachers (n = 50) for their feedback and opinions.

3.1. Data Collection

For stage 1, to collect focus group and interview data, we used methods from [19] to plan, organise, and execute focus group interviews as a qualitative research technique to understand teachers’ perceptions, experiences, and opinions on mitigating and measuring student disengagement. These involved the setting of research objectives and the selection and the grouping of participants for focus group sessions; developing an interview guide that described the questions and topics to be discussed during focus group sessions; facilitating and moderating the sessions; and audio recordings of the sessions that were later transcribed using Dictanote online tools (www.dictanote.co (accessed on 3 October 2023)). The transcripts were then manually vetted using the audio recordings for spelling or grammar corrections; typical spelling amendments were related to names as the participants referenced or addressed each other during the sessions. Grammar corrections were neglected because the understanding and context of the communication that the participants provided were sufficiently clear.

At stage 2, the student researcher observed participant behaviour, verbal feedback, hand gestures, and user experience data from the AR application sessions for each of the participants, such as the time taken to complete the tasks (sheep-shearing AR app), movement and direction (Garden of Eden AR app), and interactions (forest adventure AR book).

A postsession survey feedback form for stage 3 included the themes collected and discussed after the conclusion of the focus groups and interview sessions. Feedback was collected using a standard 5-point Likert scale to state its level of agreement.

3.2. Focus Group Questions

A set of questions was generated as focus group questions based on the system framework put forth by [20]. Questions targeted the following components, known to provide evidence through responses from face-to-face sessions: determining, reporting, improving, influencing, and assessing student disengagement activities and outcomes achieved during a typical class session. These focus groups questions and interviews, together with the introduction of AR to teachers, also served to iteratively refine augmented reality experiences to improve their value in addressing the disengagement of students with the guideline below:

• Identify the signs and the cues of a student who is disengaged in class.
• Mitigate: methods and strategies educators use to influence engagement in class.
• Measure: reporting on the level of student engagement and tracking it from lesson to lesson.
• AR tools: opportunities for young students to self-engage, e.g., AR book app.
• Collaborate: students collaborate with one another, which allows them to transfer the level of engagement skills.
• AR analytics: related measures of augmented reality experiences during class lessons using an AR-enhanced analytics.

The observed AR experience consisted of a short introduction to AR and the use of three AR applications by the educators, namely, the sheep-shearing AR application, the forest adventure AR book, and the Garden of Eden AR experience. Each of these AR applications promoted various opportunities that teachers could use to plan, create, and deliver lessons using AR tools, especially for young students. The researcher documented the verbal and gestural responses and feedback from teachers when the AR applications were used.

The postsession educator feedback form comprised two portions: (1) ten questions requiring responses on the level of importance of methods and strategies in mitigating student disengagement, and (2) five questions requiring responses on the level of importance of methods and strategies in measuring student disengagement discussed during the focus group and interview sessions. The postsession educator feedback form was sent to 50 teachers to collect feedback on the themes identified during the focus group sessions.

4. Results

4.1. Participants

Eight teachers (n = 8) who were full-time educators at three schools who taught subjects at the primary school level participated in this focus group study. Invitations to participate in the study were initially sent to twenty educators teaching grades 1 through 3 in the three schools. Although nine teachers responded to share their insights through research, eventually eight educators participated, with one withdrawing for personal reasons. Teachers represented the range of grade levels 1 to 3 and were educators comfortable using technology in their lessons. The eight educators had previous experience using AR in a personal or educational setting.

Fifty teachers (n = 50) responded to the prepared feedback form to share their perspectives on the themes collected from stage 1 of the study. These were collated through printed forms sent via email and returned to the researcher, as well as an online version of the feedback form for teachers who preferred to return their feedback via online means.

From the general qualitative feedback collected from focus groups, interviews, and observation sessions, all teachers indicated that AR applications generated analytics that could identify students who were disengaged in both classroom and outdoor learning. Consistent with the current literature, teachers agreed that the use of AR in classrooms increased learning outcomes and could keep students engaged, motivated, and interested during lessons. However, for AR applications to identify, mitigate, and measure student engagement, useful data needed to be collected, analysed, and presented. Effectively, in order to enable this functionality, the integration of mobile device sensors like ambient light sensors, proximity sensors, motion detectors, accelerometer, gyroscope, and magnetometers is required when developing AR applications; the data analytics gathered from these sensors is defined as AR-enhanced analytics (AREA).

4.2. Teachers’ Focus Group Sessions

This section discusses the main findings of the teachers’ focus group interviews. Each of the interview questions was used as a discussion topic to facilitate teachers’ attention in better understanding their perception and experience in mitigating and addressing student engagement during class lessons. The conclusions and recommendations based on the focus group data are shared in the following section. Few differences were noted between the responses of educators teaching high school, primary, and prep school levels; where necessary, a common terminology is presented for understanding the mitigation and measure of student engagement according to what was used across the various levels of student groups.

The focus group session and the interviews shared several signs and signals that teachers used to determine if a student was participating and was engaged in class lessons, and how teachers could enhance student engagement using AR technology. “Using digital resources in learning, like online tools or apps, can make studying more interesting and accessible… like adding AR technology with analytics help to better understand the student’s engagement levels”. As one teacher put it: “It will be wonderful if AR can show how long my student is looking at a particular page from a book” and “…indicate which sections the student is reading from…”.

4.2.1. Constant Conversation

Teachers interviewed had the opinion that a student was engaged in a class lesson when he or she was observed to have an ongoing conversation with the teacher or with another student during lessons, and in general, one-to-one sessions were preferred over group sessions. However, this resulted in other students being “left-out” of the discussion or conversation; primarily, the teachers felt that they could not manage conversing with the whole class at the same time. The teachers shared several obstacles which could be overcome with the use of AR applications and tools in classrooms for enhancing engagement levels amongst students; one of these was the ability to provide each student customised and personalised lesson materials according to their learning levels. An AR application provides the ability to interact individually with each student on their own personal mobile device, managing the learning process. Some of the suggestions of teachers were to create and enable learning virtual characters that complemented the teacher in initiating constant conversation with each student individually during lessons. “Talking to students often helps keep them interested and helps you understand how well they’re engaging with the class. But, to get the full picture, it’s good to mix in other methods, like asking for feedback or paying attention to non-verbal signs”.

4.2.2. Student Reflection and Thoughts

During constant conversations with the teacher or fellow students, the context on the conversational topics and phrases in which students engaged was an important factor and had to be monitored and recorded. Often, this happened at the end of a class lesson, where students were encouraged to share annotations or short phrases about their understanding of the lessons. These could be noted in written or audio form for the lesson reflections using emotional phrases such as “I liked the lesson because…”, “I was confused”, or “I did not understand”, etc. Teachers noted that positive student reflections increased levels of engagement during class lessons. Teachers were only able to read the written or audio reflections after class, and it would take a considerable amount of time to vet the entire class reflections and objectively provide quick responses and interventions to address students’ needs. AR applications on individual mobile devices can alleviate this limitation by requiring students to record their learning journey directly through audio or written responses and processing the responses to provide insights to teachers almost instantaneously to assist teachers with responses to the reflections and feedback.

4.2.3. Reading Enjoyment

Teachers observed that students who read mindfully with little conversation amongst their class peers were engaged in their lessons. This was particularly true during the class lesson, where reading was encouraged to better understand the topic of study. Students who leaned forward while reading or during conversations gave indications of engagement in the learning activity. Teachers noted that books that complemented complementary or external elements to explore made the student curious and ready to explore, such as science experiments or apparatus which required students to read and set up. The students were involved with multiple tasks and were not be bored. As teachers interacted with the AR book, their response was that the use of AR technology on the story book helped to increase the engagement in the reading and book characters with the young students. “Regular reading not only enhances engagement but also improves vocabulary, AR applications can give measured indicators of students responses and more personalised”.

4.3. Mitigating Disengagement with AR-Enhanced Analytics (AREA)

The focus group members subsequently shared signs and cues of students who were not engaged in class lessons and how teachers could utilise AR technology to mitigate and improve student engagement.

4.3.1. Negative Facial Expressions

All teachers interviewed related that a student’s facial expression give very important cues to the engagement level of students. Teachers spoke of students who are disengaged while having a blank or neutral face expression, while some students constantly frown and look away, constantly giving an indictment of boredom or lack of interest. A lack of eye contact was another highlighted key aspect of disengagement from teachers: not looking at the teachers, staring at the ceiling or wall, yawning and sleepy eyes, constantly rubbing eyes. Three teachers also clarified that not all neutral facial expressions were associated with disengagement, as some students naturally assert a neutral facial expression during class. The feedback from the teachers was: “AR analytics measuring how often students look away is useful for teachers, giving teachers the ability to identify by a students who lose focus in class”. “AR analytics provides teachers with essential information to pay attention to patterns and address them accordingly, adjusting teaching methods if needed”.

4.3.2. Difficulties with Reading

All teachers in the focus group agreed that students with reading difficulties can be disengaged from class lessons or learning activities. For a student who refuses to read a book, group reading sessions facilitated by the teacher, where students share feedback and reflections with one another on a story book, can be encouraged. The prep level teachers noted that visual storybooks encouraged participation with younger students, influenced reading, and encourage good reading habits. They felt that this would ultimately improve reading skills and help increase engagement in classroom lessons. One teacher used cue cards as a method to encourage reading, starting with a cue card of a picture, followed by a cue card with a picture and a text description of the picture, similarly to maths concepts of numbers and counting objects. One teacher shared that “keeping reading tasks measurable helps create a connection with book, shows engagement, and promotes a focused reading environment”.

4.4. Student Disengagement vs. AR-Enhanced Analytics (AREA)

The themes identified from the focus group session were related to the suggestion given by teachers on how AR-enhanced analytics could provide the requirements and needs to mitigate student engagement, as shown in

Table 1. Student Disengagement vs. AR-Enhanced Analytics (AREA).

Identified Themes	AR-Enhanced Analytics (AREA)
Task Time	Teachers want to know the amount of time that a student is on and off tasks during lessons. AREA utilises interaction analytics to capture the tapping and swiping of fingers on mobile devices’ screens and other sensors to collect timing-specific data in seconds and minutes that can determine on-task duration.
Movement	Teachers want to know how a student interacts and engages with other students, apparatus, and groups during class lessons. AREA leverages the surrounding spatial analytics from the mobile device sensors to provide location, speed, and orientation data that can give a precise measure of the amount of student movement taking place at a specific time.
Reflections	Teachers need details of student reflections and feedback to address student disengagement during class rather than after class. AREA utilises interaction analytics with the AR application to capture students’ notes, feedback, and responses to inform teachers on students’ learning progress.
Assessment	Teachers require marks and scores from tasks, quizzes, tests, etc., to be quickly made available. AREA uses learning analytics to record marks and score allocations to present to teachers.
Collaboration	Teachers want to record the process of collaboration work amongst their students. AREA provides the capabilities to collect, record, and present data on duration for tasks where students are working together.
Emotions	Teachers are always concerned about the well-being of their students. Emotional and physiological data from students can be collected using AREA sensory analytics to better inform teachers about their students’ emotional state. Mobile device sensors working together with wearables can collect real-time data.

.

4.5. Teachers’ AR App Usage Observations

During hands-on use of AR, five of the eight teachers reported that the use of multimedia technology improved engagement, and the use of “time-checking” technology to determine the amount of time the student was on task could help teachers measure the level of engagement of the student. Two other teachers were of the opinion that multimedia technology was a distraction and could lead to students being “side-tracked” during lessons if not closely monitored on the use. The last teacher felt that the use of multimedia technology like AR could provide accurate measures of engagement levels by recording the amount of time students were on tasks and also making lessons more interesting with the use of animations and digital overlays, such as AR.

4.6. Feedback Questionnaire Results

A total of 50 teachers participated in the focus group study. A look at the teaching levels showed prep-level teachers represented 44.0% (n = 22) of all the participants followed by primary-level teachers, at 30.0% (n = 15), while only 26.0% (n = 13) taught at the high school level. When analysed by subjects, a majority taught English (46.0%, n = 23), followed by maths (30.0%, n = 15), and science (24.0%, n = 12). We note that more than half (66%, n = 33) of the teachers had used AR before but only 18% (n = 9) had used it in class. (see to

Table 2. Sociodemographic variables (n = 50).

		F	%
Grade Level	High school	13	26.0%
	Prep	22	44.0%
	Primary	15	30.0%
Main Subject	English	23	46.0%
	Maths	15	30.0%
	Science	12	24.0%
Used AR	No	17	34.0%
	Yes. Not in Class	24	48.0%
	Yes. In Class	9	18.0%

).

In a quantitative evaluation of the use of AR analytics to mitigate student disengagement, several dimensions were analysed to understand its acceptance and perceived effectiveness by teachers in a classroom setting (

Table 3. How teachers mitigate students’ disengagement.

	N	Minimum	Maximum	Sum	Mean	Deviation Std.
Conversations	50	3.00	5.00	218.00	4.3600	0.69282
Movement	50	2.00	5.00	218.00	4.3600	0.82709
Notes and reflections	50	2.00	5.00	211.00	4.2200	0.93219
Eye contact	50	1.00	5.00	205.00	4.1000	1.03510
Positive relationships	50	2.00	5.00	219.00	4.3800	0.83029
Technology	50	1.00	5.00	222.00	4.4400	0.95105
Collaboration	50	1.00	5.00	219.00	4.3800	0.87808
Reading	50	1.00	5.00	217.00	4.3400	1.06157
Discovery/exploratory learning	50	3.00	5.00	218.00	4.3600	0.80204
Rewards	50	1.00	5.00	196.00	3.9200	1.35285
Valid N (listwise)	50

Teachers shared their feedback and perspectives on the use of AR analytics to mitigate and measure disengagement among school students.

). The first set of dimensions, including conversations, movement, positive relationships, technology use, collaboration, and discovery/exploratory learning, demonstrated high mean scores ranging from 4.36 to 4.44. These scores indicate a strong agreement among teachers about the positive impact of AR in these areas (standard deviations ranging from 0.69 to 0.95). This finding suggests a consistent and positive perception of AR analytics in improving interactive and collaborative aspects of teaching.

To measure student disengagement (

Table 4. How teachers measure students’ disengagement.

	N	Minimum	Maximum	Sum	Mean	Deviation Std.
Time on tasks	50	2.00	5.00	227.00	4.5400	0.78792
Reflections	50	1.00	5.00	205.00	4.1000	1.12938
Attention span	50	2.00	5.00	202.00	4.0400	0.92494
Feedback	50	2.00	5.00	219.00	4.3800	0.85452
Use of technology devices	50	2.00	5.00	209.00	4.1800	0.91896
Valid N (listwise)	50

), the key metrics that stood out were mainly time on tasks and feedback. For time on tasks, a compelling 75.0% (n = 15) of teachers strongly agreed that this was an effective metric, with another 25.0% (n = 5) in agreement. For the feedback, 60.0% (n = 12) of teachers strongly felt feedback was a reliable indicator, coupled with 35.0% (n = 7) who agreed.

The one-way ANOVA and the Kruskal–Wallis H test showed that there were differences between teachers’ levels of teaching in terms of conversations and use of technology devices K(2,20) = 8.199, p = 0.017, K(2,20) = 6.079, p = 0.048. In terms of conversations, prep teachers agreed more compared to other levels (M = 4.11, SD = 0.333), while for the use of technology devices, high school teachers agreed more compared to other levels (M = 4.444, SD = 0.881).

There were no conclusive findings to support that there were statistically significant differences between teaching subjects and teachers’ perspectives on the use of augmented-reality analytics in the engagement of primary school students. While the Kruskal–Wallis H test showed no statistically significant differences in the grades, the one-way ANOVA showed that there was a statistically significant difference between grades and positive relationships, F(2,19) = 4.831, p = 0.022, where English teachers showed the highest mean score compared to maths and science teachers, (M = 4.272, SD = 0.786), see Appendix A.

5. Discussion

The focus group findings investigated teachers’ perspectives on the use of augmented-reality analytics in the engagement of primary school students. Several significant insights were drawn from the data.

5.1. Teachers’ Strategies for Mitigating Students’ Disengagement

Teachers identified various strategies to address student disengagement. Among these, movement and technology emerged as the most popular techniques, with an overwhelming majority (80.0%) of teachers strongly agreeing on their efficacy. This finding aligns with previous research that has established the positive impact of technology, especially augmented reality, in enhancing students’ engagement levels [21]. The high preference for conversations and rewards also reinforces the importance of teacher–student interactions and the motivational role of rewards in the educational setting [22].

5.2. Teachers’ Metrics for Measuring Students’ Disengagement

The results of the qualitative study underscore the importance of evaluating student participation using different metrics. A significant majority of teachers deemed “Time on Tasks” (75.0%) and “Feedback” (60.0%) as crucial indicators of student engagement. The high emphasis on “Time on tasks” can be linked to the literature that suggests that students who spend more time on learning tasks tend to be more engaged and show better academic performance [2]. “Using AR analytics to keep track of how much time you spend on tasks in class helps you make sure that you are keeping involved and not getting bored or disinterested”. Similarly, feedback, when constructive, has been shown to foster a positive learning environment and improve student engagement [23].

5.3. Differences Based on Teaching Grades/Levels

The data suggest a difference in perspectives based on teaching levels, especially concerning conversations and the use of technology devices. Such differences highlight the need for tailored strategies and tools for different grade levels, ensuring maximum effectiveness. For instance, the higher agreement among high school teachers on the use of technology devices might be due to the age-appropriate use of such devices in higher grades [24].

5.4. Differences Based on Teaching Subjects

Although the study did not find conclusive evidence of differences based on teaching subjects, there was a notable distinction in positive relationships between English teachers. This suggests that subject-specific pedagogy could influence the perceived effectiveness of augmented-reality analytics. As the literature indicates, subject-specific teaching strategies, especially in English, emphasise relationship building and discussion [25].

The findings underscore the potential of augmented-reality analytics in the mitigation and measure of student disengagement in primary school students. While there is a general consensus on the efficacy of several strategies and metrics, variations based on teaching levels and subjects suggest the need for a more nuanced approach in implementing these tools.

6. Conclusions

The study, while offering valuable insights, comes with certain limitations. Qualitative findings were mainly drawn from the perceptions and experiences of a select group of eight primary school teachers, potentially limiting the generalisation to a broader educator population. The feedback form from 50 teachers provided the quantitative analysis to this study and the answers to the research questions; teachers strongly agreed to the application of AR analytics in mitigating and measurement complementary tools to address student disengagement. This was evident through the feedback collected and also the responses when teachers used AR applications. This sample size, coupled with its focus on primary education, might not resonate universally across different educational spectrums. Additionally, educators’ varying degrees of technological familiarity with AR might have influenced their perceptions, potentially skewing the results. Those less familiar with the technology may have encountered difficulties in its integration. Furthermore, while the mixed-methods approach employed provided a holistic understanding, it might not have captured specific nuances, particularly within the quantitative data. The themes were collated from the specific group of eight teachers in the focus group and did not represent the overall population of teachers faced with student disengagement challenges in classrooms.

In terms of implications, the research has profound theoretical and practical ramifications. Theoretically, the need to revisit traditional student disengagement metrics is highlighted from a teachers’ perspective in light of AR-enhanced analytics usage. Furthermore, the study accentuates the pivotal role of AR technology and utilising AR analytics in reshaping pedagogical paradigms, merging conventional with contemporary strategies. The curriculum also deserves to be redesigned to incorporate AR and utilise AR enhanced analytics, fostering better measures of student disengagement. As we look ahead, future research directions could range from engaging a diverse educator sample across varied educational settings to longitudinal studies gauging analytics gathered from AR analytics and teachers’ preferred AR applications’ long-term impact and use.

The research underscores the potential of AR analytics as an instrumental tool in helping educators identify, measure, and address student disengagement. Feedback from educators indicated that AR applications, when effectively integrated into the classroom, while enhancing student engagement, can also provide real-time analytics to benefit further student learning insights (motivation, interest, etc.). However, to be truly effective, AR must be combined with traditional teaching methods, and educators must be adequately trained to use it. As technology continues to evolve, educators must adapt and integrate these emerging tools seamlessly into their teaching methodologies, ensuring that students remain at the core of the learning experience.