The Influence of Tutor Engagement, Attention, and Positive Emotions on Student Achievement

ABSTRACT

Reading is an essential skill learned in early elementary years. Phonological decoding, the ability to sound out written words, is central to the cognitive process of reading. Deficits in phonological decoding can lead to low comprehension. Research shows that higher levels of attention, engagement, & positive emotions from teachers lead to better student outcomes. The present study explores the roles that tutor emotion, attention, & engagement play in aiding student phonological outcomes after reading intervention using facial expression analysis software, iMotions. iMotions was used to quantify rates of tutor engagement, attention, & emotion toward a student across four tutoring sessions. We explored how prevalent tutor attention, engagement, & emotions are and how all variables are related. Student phonological decoding ability was measured before and after tutoring. As engagement requires active participation beyond attention, we hypothesized that tutor engagement, not attention, during a reading intervention would be positively associated with reading outcomes. Results indicated that tutors exhibited attention for a higher percentage of time than engagement across all tutoring sessions (80.6% and 43.6%, respectively). Attention and engagement were not significantly correlated (p = .675), corroborating the notion that the two are unique measures of tutor involvement. While students’ phonological decoding skills improved after tutoring, the change in scores was not associated with rates of tutor engagement.

INTRODUCTION.

Reading, a mental process used to obtain and interpret written information, is an important skill that is related to children’s later academic achievement and health outcomes [1-3]. A current shortcoming of education in the United States is the declining reading scores across all grades; 69% of fourth grade students are reading under proficient level according to the National Assessment of Educational Progress (NAEP) in 2024, compared to 65% in 2019 & 71% in 1992 [4]. A central goal of elementary-school teachers is to foster adept readers who are motivated to read in order to efficiently learn and grow. Thus, honing fundamental reading skills, specifically phonological decoding, is a critical focus during elementary school. Phonological decoding, the ability to sound out written words, is key to understanding the overall meaning of text [5]. Early reading intervention is a way to proactively limit potential challenges a reader may face in education [6].

Student performance is affected by the instruction they receive [7]. Teachers and tutors shape students’ relationships with learning as their feedback and overall mood greatly influence the quality of instruction and experience in the classroom as well as children’s motivation, energy, and social engagement [8,9]. A poor classroom environment may contribute to a lessened student-teacher relationship, which can result in low student participation and learning. By pointing out the successes of the students and encouraging them to read challenging texts, a teacher often helps the student develop a positive self-concept with reading [10]. Therefore, teachers who demonstrate higher attention, engagement, and positive emotions during the lesson are more capable of supporting the student, which assists future development.

Early elementary students are easily affected by the emotions and attention their teachers show. In a classroom setting, the phrase “paying attention” is often used as a measure of learning and is considered to be interchangeable with engagement. However, engagement and attention are unique measures of achievement. Although they are closely related, engagement involves active participation while attention only requires being focused on a stimulus without any further action [11]. Compared to engagement, which is reserved for challenging and/or enjoyable activities, attention is used continuously throughout the day, making it important for learning [12]. Attention increases efficiency and directs concentration so the individual can recall things and engage with the material or task at hand [12]. Beyond attention, engagement is uniquely important because it involves energy and motivation and is positively associated with academic achievement [13].

Existing research on the relationships between tutor attention, engagement, & emotions is limited [14]. The purpose of the current study was to investigate how these variables correlate with student outcomes, after a reading intervention. Using facial analysis software allows for objective categorization of emotions in real tutoring sessions. The tutors in the current study administered the Road to Reading (RTR) program, a phonetic reading instruction program that focuses on building phonetic decoding skills in kindergarten through fifth grade students. The current study utilizes recordings of tutors administering RTR to analyze their emotions, attention, and engagement during tutoring sessions and how these measures correlate with student learning outcomes after tutoring. We aimed to answer the following research questions: (1) How often do tutors exhibit attention, engagement, joy, neutral, surprise, and confusion across tutoring sessions?; (2) How are attention and engagement related to each other & tutor emotion during tutoring?; and (3) How are tutor engagement and attention related to changes in phonological decoding following tutoring?

We hypothesized that tutors would exhibit attention more often than engagement across all tutoring sessions, and that joy would be the most prevalent emotion compared to neutral, surprise, and confusion. We expected tutor engagement and attention to be uniquely related to tutor emotions. Specifically, we hypothesized joy would be highly correlated with engagement and neutral would be inversely related to engagement and positively related to attention. Finally, we hypothesized tutor engagement, but not tutor attention, during a reading intervention would be positively related to reading outcomes after tutoring.

MATERIALS AND METHODS.

Participants.

Students, also referred to as tutees, were recruited as part of a larger reading intervention study using advertisements in schools and clinics in a metropolitan city in the southeastern United States. The present study analyzed facial expressions of five tutors administering a reading intervention to four first-grade students (mean age: 6.56 years, SD: 0.43; 3 male). Three out of four students identified as white, and one student identified as more than one race. No students identified as Hispanic or Latino. All tutees were given informed consent and signed a photo release. Participant information was de-identified during data collection. Students qualified for tutoring based on their Test of Word Reading Efficiency (TOWRE) Scores [15], which is a measure of word reading and phonological decoding. Students that scored in the bottom third of all participants tested were selected for tutoring. This resulted in a total of 8 participants who were randomly split into 2 groups—tutoring and waitlist control. The 4 participants that received tutoring first were included in the current analysis.

Tutoring took place over Zoom (Version 5.6, 2021) for 6 weeks (5 days/week, 40 minutes/day) with the tutor and student both in their private homes. All tutors were Caucasian females completing their master’s degrees in special education, who completed training and internal fidelity assessments. Students completed the standardized Word Attack subtest from the Woodcock-Johnson IV Test of Achievement [16], a phonological decoding test, before and after tutoring, answering questions until they got 6 incorrect answers in a row. Raw scores were transformed to W scores (200-800 range, age-adjusted M = 500).

Tutoring Sessions.

Tutoring sessions were recorded on Zoom in the spring of 2021, following a standardized lesson plan, derived and modified from the RTR curriculum (described under the Tutoring Curriculum section). Some sessions included progress monitoring probes that evaluated the students before, during, and after each level. When this occurred, the final steps (i.e., four and five) were sometimes cut short or eliminated entirely. All students started tutoring at the lowest reading level and advanced at their own pace throughout the duration of tutoring.

Tutoring Curriculum.

Step 1 took 3 minutes and had students practice the vowels and consonant blends they learned in previous tutoring lessons by naming the vowel/consonant blend, stating the sound it makes, then using it in a word. Step 2 took 10 minutes; Students were given a word and instructed to put the word together using letter tiles, then read all the tiles together. Step 3 took 10 minutes; Students were instructed to read each given word smoothly, or without sounding it out beforehand. If students did not read the word correctly, the tutor explained where the student erred and put the word into a separate pile to read again at the end. Step 4 took 5-7 minutes; Students set up categories on a whiteboard as the tutor gave them 10 words. Categories were chosen by the tutor and the number of categories ranged from 2- 4. Step 5 took 5-10 minutes. Students read a short story at their own pace to understand how the words learned during the session appear in context.

Data Processing & Analysis.

Each tutor video was cropped into sections, corresponding to the start and end time of each step using Sony Vegas Video Editing Software (MAGIX Software GmbH, 2024). Cut clips were uploaded to iMotions (Version 10, 2025) for analysis. iMotions includes a feature that allows researchers to analyze human behavior using facial expression analysis. Upon uploading videos to iMotions, the area for video analysis was cropped to include only the tutors’ face. After the video was fully processed, emotion and facial expression categories were selected for analysis. Joy, surprise, engagement, confusion, and neutral were chosen in the Emotion category, and attention was chosen in the Facial Expression category. iMotions categorizes engagement as an emotion and attention as a facial expression based on internal metrics; however, engagement will be referred to as a facial expression, rather than emotion, for the current study. Sensor data was exported to Excel and cleaned to only display the emotion and facial expression percentages, timestamps, sample number, and video coordinates.

The first and last video per tutoring level for each student was used. When lighting or data quality prevented analysis, the second or second to last video per reading level was used.

Data Analyses.

Data was exported from iMotions. This data included a number quantifying iMotions’ confidence that the tutor was showing each emotion/facial expression chosen for analysis, ranging from 0-100%, at every timepoint, extracted every 33 milliseconds. Multiple emotions could be present at one timepoint if they were comprised of similar facial movements. Timepoints during which iMotions could not detect any facial expressions/emotions were excluded from analyses. To calculate the true amount of time an emotion or expression was shown, we used a threshold of 75% confidence in Excel. Time points where iMotions’ confidence was over 75% were included as a timepoint in which the emotion or facial expression was shown. To calculate the percentage of time each emotion or expression was exhibited by the tutors, the timepoints when the emotion or expression was exhibited, were divided by the total number of timepoints in the video clip.

iMotions identifies attention based on head and eye orientation. Engagement is determined by head/eye orientation as well as unique facial muscle activations. Similarly, the emotions of joy, surprise, neutral, and confusion are calculated by the unique combination of facial, head, and eye muscle movements; for example, joy combines cheek raising and a lip corner pull. See [17] for other emotion calculations.

For the first research question, we measured how often tutors exhibited attention, engagement, and the emotions of joy, neutral, surprise, and confusion. For the second research question, we examined how emotions of interest were related to attention, engagement, and to each other. The relationships between variables were quantified using Pearson’s correlation. The following relationships were examined: attention and engagement, attention and neutral, attention and joy, engagement and neutral, engagement and joy, and neutral and joy. For the final research question, we examined how tutor emotions, engagement, and attention related to tutoring outcomes. To determine the relationship between engagement, attention, and reading outcomes, the changes in students’ phonological decoding scores were correlated with tutor attention and engagement using Pearson’s correlation.

RESULTS.

How often do tutors exhibit attention, engagement, joy, neutral, surprise, and confusion across tutoring sessions?

Tutors exhibited attention more than engagement, 80.6% and 43.6% of the time, respectively (Figure 2). Neutral was the most prevalent emotion across tutoring sessions, 60.6%, followed by joy (24.3%), surprise (3.1%), and then confusion (0.3%).

How are attention and engagement related to each other & teacher emotions during tutoring?

Attention and engagement showed a non-significant correlation (r = -0.114, p = 0.675), demonstrating that attention and engagement are unique processes that tutors exhibit during tutoring. There was a similar non-significant relationship shown between attention and joy, as well as between attention and neutral (r = 0.117, p = 0.667; r = 0.209, p = 0.437, respectively). This suggests that as the tutor shows more attention, they are not showing a related amount of joyful or neutral facial expressions. However, engagement and joy were positively correlated across all tutoring sessions (r = 0.734, p < 0.001; Figure 3a). Figure 3 demonstrates that as the tutor shows more engagement, the tutor also shows more joyful facial expressions. Similarly, engagement and neutral were negatively correlated across all tutoring sessions (r = 0.929, p < 0.001; Figure 3b), showing that the tutor exhibited more engagement, the tutor showed less neutral facial expressions (r = -0.765, p < 0.001).

How are tutor engagement and attention related to changes in phonological decoding following tutoring?

All students showed improvement in their Word Attack test score (Figure 4), averaging an 18.25% increase from pre- to post-tutoring. To examine whether tutee changes in phonological decoding scores were correlated with tutor attention and tutor engagement, separate correlations between change in phonological decoding score & tutor attention and engagement were run. Children’s change in phonological decoding score was calculated by subtracting their post-tutoring score from their pre-tutoring score, such that larger change scores correlated with greater growth. There was no significant relationship observed between phonological change scores & tutor attention or engagement (r = -0.233, p =0.767; r = -0.656, p = 0.344).

DISCUSSION.

This study aimed to determine how often tutors exhibit engagement, attention, and specific emotions during tutoring sessions using iMotions. Further, this study explored how tutor attention and engagement are related to tutor emotions. Finally, this study investigated how tutor attention and engagement related to student reading ability before and after intervention. Student engagement is viewed in two ways: an accountability measure of involvement with learning environments and a variable in research to understand and predict student behavior [19].

One way to observe student engagement is by first looking at how the teacher engages with them to determine how justified student engagement is. In the current study, we explored whether measures of tutor engagement uniquely contributed to student learning outcomes.

For our first research question, we assessed how often tutors exhibit attention, engagement, & the emotions of joy, neutral, surprise, and confusion across tutoring sessions. We hypothesized that tutors would exhibit attention more often than engagement, and that joy would be the most prevalent emotion [18]. Results indicated that tutors exhibited attention more often than engagement and any emotion (Figure 2). Further, neutral was most prevalent emotion shown by the tutors throughout tutoring. Although contrary to our hypothesis, this result is in alignment with previous research, reporting that neutral face expressions are the most common face expression exhibited by tutors [19]. Given this pattern of emotions is present across all tutors suggests it is relatively stable and may generalize to other tutoring sessions, though more research is necessary to support this notion.

We also asked how attention and engagement were related to each other & to other tutor emotions during tutoring. The amount of time tutors showed attention and engagement was not significantly correlated, indicating that attention and engagement are unique measurements of tutor involvement, likely affecting the student differently. During learning, we expected engagement to be more related to joy, compared to surprise, confusion, or neutral. Our results support this, displaying a strong positive correlation between engagement and joy, and a strong negative correlation between engagement and neutral. Attention was not significantly correlated with joy nor neutral. These results emphasize the importance of engagement over attention for student emotions [20]. The strong engagement-joy correlation is in line with previous research showing a positive teacher-student relationship is positively associated with joy [18]. The engagement-neutral correlation is in line with previous literature showing the more engaged the tutor appears, the less neutral facial expressions they will show [21].

Additionally, we examined whether changes in phonological decoding ability following tutoring were related to tutor engagement and attention. Word Attack scores improved for every student throughout tutoring (Figure 4). We expected a positive relationship between these variables, suggesting teacher support fosters strong student engagement, leading to high student achievement [22]. However, neither tutor engagement nor attention correlated with reading scores following tutoring. Our null result might be explained by possible program limitations that include poor lighting and unintentional skin tone bias.

Limitations

The videos included in these analyses were intended to be used as checkpoints for growth; therefore, this study shows a subset of the data. The first and last day of each level were used to depict progression, yet some videos had inconsistent lighting which made iMotions unable to reliably analyze faces in variable lighting conditions. Poor video quality resulted in some videos being eliminated from data analysis. Future studies should ensure consistent lighting and high-quality video recordings, when possible, to allow for maximum trial retention. Additionally, future studies should aim to increase the sample size to ensure the relationships observed here extend beyond this sample size.

CONCLUSIONS.

The present study examined the relationships between tutor engagement, attention & emotions on student learning during a reading intervention. Results indicated attention and engagement are different emotions/facial expressions exhibited by the tutor, and that these expressions are uniquely related to tutor emotions. Our results did not support hypotheses that these expressions are uniquely related to student outcomes. However, future studies should use larger sample sizes to further explore this relationship. Future analyses examining lesson-specific probes may improve our ability to discern a relationship between positive emotions, engagement, and reading growth. This preliminary study points toward the use of facial recognition technology as a means to acquire and analyze real-time tutor/student interactions.

ACKNOWLEDGMENTS.

This work was supported in part by NIH grant R37HD09551. I would like to acknowledge Dr. Deweese for being the best advisor and thank Addison and Sarah, my mentors, for their support through my research. Thank you to Dr. Cutting for accepting me to the EBRL Lab.

REFERENCES.

1. B.S. Rahmasari, Peer Tutoring: An Effective Technique To Teach Reading Comprehension. KnE Soc. Sci., 245–258 (2017).
2. A. Golbeck, A. Paschal, A. Jones, T. Hsiao, Correlating reading comprehension and health numeracy among adults with low literacy, Patient Educ. Couns. 84, 132–134 (2001).
3. H. Savolainen, T. Ahonen, M. Aro, A. Tolvanen, L. Holopainen, Reading comprehension, word reading and spelling as predictors of school achievement and choice of secondary education. Learn. Instr. 18, 201–210 (2008).
4. The Nation’s Report Card. https://www.nationsreportcard.gov/, (accessed May 2025)
5. F. Stutz, E. Schaffner, U. Schiefele, Relations among reading motivation, reading amount, and reading comprehension in the early elementary grades. Learn. Individ. Differ. 45, 101–113 (2016).
6. A. N. Burgess, L. E. Cutting, The Behavioral and Neurobiological Relationships between Executive Function and Reading: A Review of Current and Preliminary Findings. Mind Brain Educ. 17, 267–278 (2023).
7. A. Wigfield et al., Role of reading engagement in mediating effects of reading comprehension instruction on reading outcomes. Psychol. Sch. 45, 432–445 (2008).
8. J. Morrison et al., Teachers’ Role in Students’ Learning at a Project-Based STEM High School: Implications for Teacher Education. Int. J. Sci. Math. Educ. 19, 1103–1123 (2021).
9. L. McLean, C. M. Connor, Relations between third grade teachers’ depressive symptoms and their feedback to students, with implications for student mathematics achievement. Sch. Psychol. Q. 33, 272–282 (2018).
10. P. Orellana, C. Melo, P. Baldwin, S. De Julio, J. Pezoa, The relationship between motivation to read and reading comprehension in chilean elementary students. Read. Writ. 33, 2437–2458 (2020).
11. N. A. Bradbury, Do I have your attention? Attention and engagement: What are they, and do I want them? Adv. Physiol. Educ. 47, 318–325 (2023).
12. S. M. H. Shah, S. Saleem, Level of attention of secondary school students and its relationship with their academic achievement. J. Arts Humanit. 4, 92–106 (2015).
13. R. J. Collie, A. J. Martin, “Motivation and engagement in learning” in Oxford Research Encyclopedia of Education (Oxford University Press, 2019), pp. 1–45.
14. A. Frenzel, L. Daniels, I. Burić, Teacher emotions in the classroom and their implications for students. Educ. Psychol. 56, 250–264 (2021).
15. J. K. Torgesen, C. A. Rashotte, R. K. Wagner, TOWRE: Test of Word Reading Efficiency (Pro-ed Austin, TX, 1999).
16. F. A. Schrank, B. J. Wendling, “The Woodcock–Johnson IV Tests of Early Cognitive and Academic Development” in Contemporary Intellectual Assessment: Theories, Tests, and Issues, 4th Ed (The Guilford Press, New York, NY, US, 2018), pp. 283–301.
17. B. Farnsworth, Facial Action Coding System (FACS) – A Visual Guidebook – iMotions (2022). https://imotions.com/blog/learning/research-fundamentals/facial-action-coding-system/, (accessed August 2025)
18. S. Tatalović Vorkapić, D. Velan, The importance of positive emotions among early childhood educators. Teach. Dev. 27, 468–486 (2023).
19. S. Alexander, A. Sarrafzadeh, S. Hill, Foundation of an affective tutoring system: Learning how human tutors adapt to student emotion. Int. J. Intell. Syst. Technol. Appl. 4, 355–367 (2008).
20. M. J. Bundick, R. J. Quaglia, M. J. Corso, D. E. Haywood, Promoting student engagement in the classroom. Teach. Coll. Rec. 116, 1–34 (2014).
21. G. Hagenauer, T. Hascher, S. E. Volet, Teacher emotions in the classroom: associations with students’ engagement, classroom discipline and the interpersonal teacher-student relationship. Eur. J. Psychol. Educ. 30, 385–403 (2015).
22. A. M. Klem, J. P. Connell, Relationships matter: linking teacher support to student engagement and achievement. J. Sch. Health 74, 262–273 (2004).

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Posted by buchanle on Friday, May 15, 2026 in May 2026.

Tags: Attention, emotion, Engagement, Reading comprehension, Tutoring