The effect of attention control and academic motivation on perceived learning in hybrid occupational therapy education

Abstract

Introduction:

Many of the studies on learning have focused on face-to-face or online learning, and information on hybrid learning is limited. The aim of this study is to examine the predictors of perceived learning in occupational therapy students in terms of different variables in the hybrid education process.

Method:

This study, which was planned in descriptive cross-sectional design, was carried out online using Google Forms. Attentional Control Scale, Academic Motivation Scale, and Perceived Learning Scale were used in this study. Multiple linear regression analysis was performed with the stepwise variable selection method.

Findings:

The coefficient of the Academic Motivation Scale Amotivation variable, which made the greatest contribution to the variance rate explained by the regression model, was −0.407. A one-unit increase in the Academic Motivation Scale Intrinsic Motivation and Academic Motivation Scale Extrinsic Motivation variables caused an increase in the Perceived Learning Scale total score of 0.198 and 0.364 standard deviations (SDs), and a one-unit increase in the Academic Motivation Scale_Amotivation variable caused a decrease in the Perceived Learning Scale total score of 0.407 SD.

Conclusion:

The most important predictor of perceived learning is amotivation. We suggest that improving the intrinsic and extrinsic motivation and reducing amotivation in students studying at universities offering hybrid learning can be used as a strategy that increases attentional control and perceived learning.

Keywords

Academic motivation attentional control hybrid education occupational therapy perceived learning regression analysis

Introduction

Higher education is extremely important for an individual for shaping the future life. Occupational therapy education is a process that shapes the personality of occupational therapy practitioners (Mattila et al., 2018). Academic and experiential components are included in the occupational therapy education process. Occupational therapy education aims to train participants as active, self-reflective practitioners who value knowledge and experience (American Occupational Therapy Association, 2018). According to Moon (1999), students achieve this by actively reconstructing information, such as reviewing new information and relating it to what they know. According to this constructivist learning philosophy, students must be truly engaged in the learning process for new information to be meaningful and useful in the long run.

The quality of education during the undergraduate period is important for a good career in the future. The university needs to ensure that occupational therapy education uses a sound education methodology to provide the required learning outcomes (Kirke et al., 2007). Students can “understand the nature of their program” and the professional characteristics required of them by using learning outcomes. Any graduate from an institution of higher education is expected to possess some general attributes. The specific knowledge that students are expected to gain should be clearly reflected in each course’s learning objectives, including “the sorts” and “levels of understanding” (Lawson et al., 2013).

The COVID-19 pandemic has disrupted the normal functioning of various activities all over the world, including education and training. The transition to online education has led in literature to focus on variables such as perceived learning outcomes and the factors affecting education and training in this new learning environment (Baber, 2020). Within this period, while many universities switched to online education completely, some universities continued face-to-face education or implemented a hybrid learning method and involving students in the learning process and in discussions of motivation and amotivation are more important now than ever before (Feldhacker and Greiner, 2022). Hybrid learning is an approach that aims to combine the benefits of “old” and “new” teaching methods for the quality of online learning activities. Hybrid learning integrates innovation and technological developments through an online learning system with the interaction and participation of traditional learning models (Bahri, 2018). Even though the impact of the COVID-19 is going down in Turkey, it continues, and hybrid education prevails in many universities. Previous publications have shown that most of the studies regarding learning are related to face-to-face or online learning, and the number of publications about hybrid learning is limited (Powers et al., 2016; Xiao et al., 2020).

Changes in the education process with COVID-19 have had different effects, especially for the student in the field of health sciences, which has practical courses in their curriculum. The rapid change in the delivery of clinical experiences has led to mass upheaval, with the spread of postponing clinical rotations, inclusion of students in telehealth services, and changing the academic calendar. With the onset of the COVID-19 pandemic, the clinical experience for medical students has also been greatly impacted, and students have been temporarily removed from internship settings and external rotations, raising concerns about residency placement and clinical performances (Akers, et al., 2020; Rose 2020). In other studies with students from different countries, medical students reported that COVID had a negative impact on their studies, enthusiasm for learning, and work performance (Meo et al., 2020; Ye et al., 2020). Additionally, in another study, 75.99% of medical students felt that online teaching did not successfully replace clinical teaching they received through direct patient contact, whereas 82.17% thought they could not learn practical clinical skills through online teaching (Dost et al., 2020). In a study on audiology students, it was stated that in an applied department such as audiology, the COVID-19 outbreak may cause gaps in education. The results obtained from the study showed that students felt “incompetent” in terms of applied courses and clinical internships with the transition to the e-learning system. In addition, the limitations of e-learning and learning were stated as maintaining attention and not providing enough interactive participation during the online course (Olcek et al., 2022).

Perceived learning is as important as the learning level of the student. Perceived learning refers to the student’s self-assessment of the outputs and products obtained from the intrinsic change process. In addition, it has been shown that perceived learning becomes more prominent mostly in education of adults and e-learning applications (Glass and Sue, 2008). Studies have stated that teacher immediacy behaviors, student motivation, student satisfaction, perceptions of the learning environment, and academic achievement are associated with perceived learning (Lizzio et al., 2022). Studies on perceived learning have shown that perceived learning is related to factors such as motivation. The motivated learner has the inner fortitude to acquire knowledge, identify and develop talents, enhance academic performance, and adjust to the expectations of the educational environment. Therefore, educators must thoroughly examine motivation to maximize each student’s potential and ability for academic achievement (Ferreira et al., 2011).

Motivation strengthens active learning in students and supports student success. Motivation is positively associated with better social relationships, higher academic achievement, better relationships during work performance, leadership qualities, and improved psychological well-being (Austin and Mescia, 2004). According to Hartline et al. (2022), motivation for students is important, given the myriad of distractions in everyday life, such as the COVID-19 pandemic and the shift to e-learning or hybrid education due to financial problems. Motivation is the first condition of undertaking a learning task and is the mechanism that serves as the driving force for the process (Meşe and Sevilen, 2021). Academic motivation, on the other hand, is defined as a student’s willingness to participate in lessons and learning activities, and the extent of the attention and effort that the student puts into participation. In addition, it plays an important role not only in face-to-face learning but also in student-centered online learning environments (Horzum et al., 2015). Motivation may differ in conditions such as e-learning, hybrid learning, and face-to-face learning. Murday et al. (2008) have suggested that hybrid courses are generally considered more effective than their online-only counterparts. Lin et al. (2017) have investigated the roles of learning strategies and motivation on learning in an asynchronous language lesson in addition to face-to-face lessons and have found that levels of intrinsic and extrinsic motivation were lower in students taking online courses. Intrinsic motivation does not depend on an outcome that can be separated from the behavior itself. It refers to participation in behavior that is satisfying or enjoyable in nature. Extrinsic motivation is defined as the urge to continue an activity without any obligation or as a drive to achieve a goal (Legault, 2020).

Academic motivation is related to many variables, and perceived learning is one of them (Lin et al., 2017). Academic motivation can be classified into different types of motivation as extrinsic and intrinsic motivation and amotivation. Previous studies, which have separated motivation as intrinsic and extrinsic, have determined that intrinsically motivated individuals have some differences. Intrinsically motivated individuals can use many different strategies and perform better at academic level. Studies have shown that these people are also more satisfied with life activities, and their state of mind is better. It has been determined that internal resources are more effective on the university students’ general academic achievement (Ryan and Deci, 2017). Amotivation, in other words, lack of motivation, is the absence of intention or impulse to engage in an activity because of the inability to establish a possible relationship between the behavior and the activity. In case of amotivation, individuals feel that they cannot establish a connection between the actions and consequences, they feel incompetent, and they have a sense of lack of control (Karagüven, 2012).

The learning process is affected by many different parameters, besides the teaching method. Among them, attention is one of the most important parts of the learning process. Attention is a central feature of both cognitive and perceptual processes, and mechanisms of attention select, organize, and maintain the focusing information most relevant to behavior (Chun et al., 2011). Attentional control refers to the ability to select target-related information while suppressing information not related to the subject to be learned. Studies have revealed that the development of attentional control skills supports effective learning by minimizing distractions from competing information irrelevant to the task (Markant and Amso, 2022).

Although the concepts of perceived learning, attention control, and academic motivation have some interrelated features in the literature, no study has been found that examines the relationship between these three concepts (Baber, 2020; Burgoyne et al., 2022; Chen and Jang, 2010; Ferreira et al., 2011; Hytti et al., 2010).

Aim of the research

The aim of this study is to examine the predictors of perceived learning in occupational therapy students in terms of motivation and attention control in the hybrid education process in this study. In this study, perceived learning was examined as the dependent variable, whereas motivation and attention control were examined as independent variables.

Method

Research design

The ethics committee approval required for the study was granted by the relevant Ethics Committee. The study was conducted in accordance with the Declaration of Helsinki, and informed consent was obtained from all participants. Research data were collected between February and March 2022.

Participants

This study was carried out with 258 university students at the Department of Occupational Therapy, University of Health Sciences Turkey. In the process of the research, hybrid education is applied in the current faculty. Faculty members in the research team interviewed with the department students outside of class hours and gave information about the study. The reminder announcement was made twice in total over WhatsApp. In the study, convenience sampling method was used. Individuals aged 18 and over who were undergraduate students in the occupational therapy department and volunteered to participate in the study were included. Cognitive problems can be seen in neurological and psychiatric diseases (Yener and Öz, 2022). The study examines cognitive parameters such as perceived learning, attention control, and motivation. Therefore, students with a diagnosis of neurological and/or psychiatric disease were not included in the study. Students with a diagnosis of neurological and/or psychiatric disease were excluded in the study. At the beginning of the study, the students were informed about the study by the researchers. This study, which was planned in descriptive cross-sectional design, was carried out online using Google Forms. Students who accepted to participate in the study and met the inclusion criteria were included in the study.

Measurements

In the study, a general information form prepared by the researchers, Attentional Control Scale (ACS), Academic Motivation Scale (AMS), and Perceived Learning Scale (PLS) were used.

General information form

Information such as age, gender, and hobbies of the individuals participating in the study were recorded.

Perceived Learning Scale

PLS was developed by Rovai et al. (2009). The validity and reliability studies of the scale were carried out with face-to-face and online learning students. The scale consists of three factors: Cognitive (C), Sensory (S), and Psychomotor (P). Each item in original form is marked between: absolutely false (1) and absolutely true (7). The total score obtained from the entire scale ranges between 9 and 63. The internal consistency value of the Turkish form was 0.83 (Albayrak et al., 2014).

Attentional Control Scale

Derryberry and Reed (2002) developed a self-assessment tool to evaluate individual differences in attention skills related to voluntary executive functions. The 20-item assessment tool was developed to measure the ability to focus on perceptual attention, switch attention between tasks, and control thought flexibly (Derryberry and Reed, 2002). The scale is one-dimensional. Higher scores indicate a high level of attentional control (Akın et al., 2013). In Turkish version study (Akın et al, 2013), item-total correlations were calculated for item discrimination of the scale and were found to range between 0.28 and 0.45. Confirmatory factor analysis (CFA) was applied for construct validity. As a result of CFA, it was seen that the goodness-of-fit index values of the model were χ² = 426.76, SD = 164, χ²/SD = 2.60, Root Mean Square Error of Approximation (RMSEA) = 0.062, Incremental Fit Index (IFI) = 0.81, Comparative Fit Index (CFI) = 0.80, Goodness of Fit Index (GFI) = 0.91, Adjusted Goodness of Fit Index (AGFI) = 0.88, Standardized Root Mean Square Residuals (SRMR) = 0.067. The Cronbach’s alpha internal consistency reliability coefficient was found to be 0.78 for the whole scale. The Turkish version of the scale was found to be valid and reliable (Akın et al., 2013).

Academic Motivation Scale

AMS was developed by Vallerand et al. (1992) in Canada. The scale consists of 28 items. It consists of a total of seven different dimensions, each of which consists of four items, including three intrinsic motivation (IM), three extrinsic motivation (EM), and one amotivational (A) dimension. The application takes place by marking over seven degrees between 1 (not at all) and 7 (agreeing completely) in the continuation of the statements. Scores from subtests range from 4 to 28. Since the subscales are evaluated separately, the values close to 28 obtained for each subscale indicate that the dimension is high in the individual (Karagüven, 2012).

Data analysis

The data obtained with Google forms were first transferred to the Excel program. Then the numerical coding was transferred to the SPSS program. Numerical variables were summarized as mean ± standard deviation (SD), and categorical variables were summarized as numbers and percentages (frequency). The Spearman correlation coefficient was used to assess the relationship between numerical variables. In order to obtain the estimation model, multiple linear regression analysis was performed with the stepwise variable selection method. From the Linear Regression assumptions, conformity-to-normal distribution was examined by the Kolmogorov–Smirnov test, linear relationship scatter plot (Scatter Plot). Adequacy of the model with multilinearity variance increasing factor (VIF) and conditional index values, correlation between errors (autocorrelation) with Durbin–Watson (D–W) test, effective observations with Covariance Ratio, distant observations with Cook’s distance, homogeneity of variance (homoscedasticity), normal distribution of errors, and extremely distant and outlier observations are examined by residual plots (Alpar, 2011). Data were analyzed with the IBM SPSS 21 (IBM SPSS Inc, Chicago, IL, USA) package program. p < 0.05 was considered statistically significant.

Results

A total of 258 students completed the scales, but those who did not meet the inclusion criteria and those with missing data (41 students) were excluded from the study. Statistical analysis was performed with the data of 217 participants included in the study. The sociodemographic characteristics of the participants are given in Table 1.

Table 1.

Sociodemographic characteristics of the students.

Sociodemographic characteristics		N = 217 (%)
Gender	Female	189 (87.1)
Gender	Male	28 (12.9)
Grade	1	62 (28.6)
	2	48 (22.1)
	3	62 (28.6)
	4	45 (20.7)
Place of living	Home (by myself)	27 (12.4)
	Home (with family)	60 (27.6)
	Dormitory	116 (53.5)
	Other	14 (6.5)
Sleep duration	<7 hours	41 (18.9)
	7–9 hours	149 (68.7)
	>9 hours	27 (12.4)
Regular leisure/hobby	Yes	118 (54.4)
Regular leisure/hobby	No	99 (45.6)
Regular exercise/sports	Yes	41 (18.9)
Regular exercise/sports	No	176 (81.1)
Smoking	Yes	31 (14.3)
Smoking	No	186 (85.7)
		Mean ± SD
Age	Years	20.71 ± 1.61
Academic average	Score (0–5)	3.21 ± 1.86
Daily cell phone usage time	Hours	5.11 ± 2.57

SD: standard deviation.

In the study, the relationship between PLS, ACS, and AMS was examined. There was no statistically significant relationship between PLS and ACS (r = 0.052, p > 0.05). There was a moderate positive correlation between AMS_IM and PLS_C, PLS_S, and PLS_P and PLS_total (r = 0.545, p ⩽ 0.01; r = 0.546, p ⩽ 0.01; r = 0.444, p ⩽ 0.01; r = 0.596, p ⩽ 0.01, respectively) (Table 2).

Table 2.

The relationship between attentional control, academic motivation, and perceived learning.

	ACS		AMS_IM		AMS_EM		AMS_A
	r	p	r	p	r	p	r	p
PLS_C	0.021	0.755	0.545	<0.01	0.353	<0.01	−0.474	<0.01
PLS_S	0.059	0.388	0.546	<0.01	0.481	<0.01	−0.404	<0.01
PLS_P	0.063	0.359	0.444	<0.01	0.362	<0.01	−0.484	<0.01
PLS_total	0.052	0.450	0.596	<0.01	0.458	<0.01	−0.529	<0.01
ACS	—	—	−0.025	0.710	0.146	0.032	−0.147	0.031
AMS_IM	—	—	—	—	0.568	<0.01	−0.398	<0.01
AMS_EM	—	—	—	—	—	—	−0.273	<0.01

Spearman correlation analyses, p < 0.05.

PLS: Perceived Learning Scale; PLS_C: PLS Cognitive; PLS_S: PLS Sensory; PLS_P: PLS Psychomotor; ACS: Attentional Control Scale; AMS: Academic Motivation Scale; AMS_IM: AMS Intrinsic Motivation; AMS_EM: AMS Extrinsic Motivation; AMS_A: AMS Amotivation.

Since there was a statistical relationship between the PLS total score and the variables AMS_IM, AMS_EM, AMS_A, PLS_C, PLS_S, and PLS_P, they were included in the multiple linear regression analysis. As a result of the analysis made with the Enter method, the conditional index value was found to be 30,612, and a strong multicollinearity was observed as it exceeded the cutoff point of 30 (Field, 2013). Since the relationship between PLS_C, PLS_S, and PLS_P variables and PLS scale sum was close to or above 0.80 as stated by Field (2013), it was thought to be the cause of multilinearity and was excluded from the analysis. Multiple linear regression analysis was continued with the remaining three variables (AMS_IM, AMS_EM, and AMS_A). Outliers for the model obtained using the least squares parameter estimation method and the stepwise variable selection method using the AMS_IM, AMS_EM, and AMS_A independent variables and two effective observations (observations 75 and 182) were removed from the dataset.

As a result of the examination of the residual graphs of the model obtained as a result of the multivariate linear regression analysis using the stepwise variable selection method with the remaining 215 observations, it was seen that the errors were normally distributed, and there was a heteroscedasticity problem. As a result of D–W test (D–W = 1.813), it was seen that there was no autocorrelation between errors (Field, 2013). No multicollinearity was found (VIF < 5). The model in which the AMS_IM, AMS_EM, and AMS_A variables were significant, predictors were found to be statistically significant (F(3.211) = 102.831, p < 0.001). The contribution of the AMS_IM, AMS_EM, and AMS_A variables to the model showing the effect on the PLS total score was found to be statistically significant (p < 0.001) (Table 3). Three variables in the model explain 59.4% of the variation in the PLS total score (Alpar, 2011).

Table 3.

Multivariate linear regression analysis results.

PLS_total	Β	SE (β)	β	t	p	VIF	R ² (adjusted R²)	F
(Constant)	29.625	2.904		10.203	<0.001		0.594 (0.588)	F(3.211) = 102.831p < 0.001
AMS_IM	0.161	0.045	0.198	3.569	<0.001	1.607
AMS_EM	0.244	0.038	0.364	6.347	<0.001	1.705
AMS_A	−0.606	0.071	−0.407	−8.482	<0.001	1.198

SE: standard error; VIF: variance increasing factor; PLS: Perceived Learning Scale; AMS: Academic Motivation Scale; AMS_IM: Academic Motivation Scale Intrinsic Motivation; AMS_EM: Academic Motivation Scale Extrinsic Motivation; AMS_A: Academic Motivation Scale Amotivation.

Standardized regression coefficients (β) were examined to find the variable that contributed the most to the variance rate explained by the model, and it was seen that the coefficient of the AMS_A variable (−0.407) was the largest. A one-unit increase in the AMS_IM and AMS_EM variables caused an increase of 0.198 and 0.364 SDs in the PLS total score, respectively, and a one-unit increase in the AMS_A variable caused a decrease of 0.407 SD in the PLS total score.

Model:

\begin{array}{l} PLS score = 29.625 + 0.161 \times AMS_IM + 0.244 \times \\ AMS_EM - 0.606 \times AMS_A \end{array}

Discussion and implications

This study was planned to examine the predictors of perceived learning in occupational therapy students in terms of motivation and attention control in the hybrid education process. Our results showed that perceived learning and academic motivation are interrelated concepts in occupational therapy students. We demonstrated that as the intrinsic and extrinsic motivations increased and the level of amotivation decreased, perceived learning increased. In addition, the most important predictor of perceived learning is amotivation.

Intrinsic motivation is the urge to pursue an activity only for pleasure and satisfaction, whereas extrinsic motivation is defined as the urge to continue an activity without obligation or as a drive to achieve a goal. In the current study, we demonstrated that extrinsic academic motivation is a factor that increases both attentional control and learning perception. A study in the field of education has stated that positive indicators of student functioning were associated with high levels of intrinsic motivation, whereas negative indicators were associated with high levels of extrinsic motivation and amotivation (Ratelle et al., 2007).

Considering that the current study was conducted with occupational therapy students, our results seem intriguing that attention control associated with learning is not related to intrinsic but to extrinsic academic motivation. Occupational therapy is an activity-based health profession that plays an important role in promoting health and wellbeing and uses substantive and intentional activities in interventions. In the occupational therapy concept, individuals need intrinsic motivation to successfully complete a task and activity and to recover. There may be many different reasons why attentional control, an important parameter in learning, is related to extrinsic but not intrinsic motivation, in occupational therapy students who have mastered the philosophical concept that intrinsic motivation is the driver of human behavior during the undergraduate education process. One of these reasons may be that the study was conducted during the COVID-19 pandemic. Due to the COVID-19 pandemic, significant changes have occurred in education processes, including universities. As it is well known, higher education in our country and many other countries is carried out online or with hybrid system (Ryan and Deci, 2017). Academic motivation as a factor affects online learning (Horzum et al., 2015; Zaccoletti et al., 2020).

A study conducted in Portugal and Italy showed that the restrictions imposed due to COVID-19 and staying at home for a long time has negatively affected the academic motivation of students (Zaccoletti et al., 2020). In addition, fear of COVID-19 may have influenced extrinsic motivation in socially increasing anxiety (e.g., financial and social). In a study investigating the fear of COVID-19 in medical school students, it was revealed that the fear of COVID-19 in healthcare workers is high (Arpacıoğlu et al., 2021). Occupational therapy students who will work in healthcare field in the future may also experience fear due to factors such as the risk of contamination, the lack of protective materials, and the increased death rate of healthcare workers. In a study conducted among students studying at the faculty of education in Malaysia, it was determined that there was a negative significant relationship between anxiety and motivation. Gritsenko et al. (2021) have revealed a significant negative correlation between the participants’ fear of COVID-19 and age. Similarly, another study found a significant relationship between age and fear of COVID-19 (Martínez-Lorca et al., 2020). In short, we can suggest that younger university students have more fear of COVID-19. Considering that the study was conducted with university students, students might be experiencing more anxiety and fear related to COVID-19 pandemic; therefore, they may be attaching more importance to external motivation sources (e.g., passing the exam, graduating as early as possible, finding a job, and providing financial support to the family).

In the current study, we demonstrated that reducing amotivation is a factor that increases attentional control and perceived learning. Amotivation, which has been included in the model by Ryan and Deci (2017) to understand human behavior as a whole, occurs when the person cannot establish a relationship between his behavior and the result of the action. The person feels incompetent and out of control. The behavior of these people is not under their control and the person questions oneself, such as: “Why am I going to school?” These thoughts may result in the termination of academic activities after a while (Vallerand et al., 1993).

Considering that both the COVID-19 and the different education models (e.g., online, hybrid) are factors affecting the concept of motivation, it is likely that students are going to experience concerns and uncertainties about the purpose of going to school and their future after graduation. The lack of face-to-face interaction between the students, their teachers and friends due to the fear of COVID-19 contamination may lead to problems in social relationships. In this context, well-established communication practices are crucial for the success of distance learning (Abrami et al., 2012). Especially in distance education, it has been proven that high intrinsic motivation is decisive for learning success. According to self-determination theory, the satisfaction of three basic psychological needs for autonomy, competence and social relationship leads to higher intrinsic motivation (Ryan and Deci, 2017). Therefore, interventions for improving students’ autonomy, self-efficacy, and social relationship skills will be important to prevent students’ amotivation in this process, especially to increase intrinsic motivation.

In this study, we showed that as intrinsic and extrinsic motivations increased and the level of amotivation decreased, perceived learning increased. Even though most studies in the literature provide data on the positive relationship with more intrinsic motivation and learning perception, results vary. Ferreira et al. (2011) have found a positive relationship between intrinsic motivation and perceived learning. Chen and Jang (2010) have not found a significant relationship between academic motivation and perceived learning. In Hytti et al.’s (2010) study, it has been emphasized that intrinsic motivation has a negative effect, and extrinsic motivation has a positive effect on students’ perceived learning. On the other hand, less successful students have difficulty in revealing intrinsic motivation skills such as goal setting, verbal reinforcement, self-reward, and punishment control techniques. The present literature shows that students with strong motivation will be more successful and will tend to learn more in web-based courses than students with less motivation (Eom et al., 2006).

In this study, it was determined that the variable that contributed the most to the perceived learning variance rate was amotivation. Lack of action-related intentions is one of the characteristics of amotivation. Amotivation is defined as the absence of both intrinsic and extrinsic motivators. It has been stated that people who are amotivated are unable to connect their own activities and learning results and may sense a loss of control or even ineptitude (Vallerand et al., 1992). Students who lack motivation think they cannot accomplish a goal, do not have the necessary skills, or do not value the activity. In literature, when the intrinsic, extrinsic, and unmotivated subdimensions of academic motivations in online learning are examined, we can see that intrinsic motivation is more prominent. Intrinsic motivation explains 47% of the latent variance of academic motivation. Intrinsic motivation means doing something because it is interesting or fun, and intrinsically motivated individuals do something to satisfy or entertain themselves (Ryan and Deci, 2017). Another study has found a positive relationship between readiness for online learning, academic motivation and perceived learning (Horzum et al., 2015). These findings show that increased online learning readiness and academic motivation increase perceived learning level. These results are consistent with the findings that academic motivation are positive predictors of perceived learning (Ferreira et al., 2011; Hytti et al., 2010). In this context, our results showed that motivation positively supports perceived learning, in line with the literature.

Limitations and strengths

The limitation of the study is that the study was carried out in a single university and in a single department, and therefore the results could not be generalized. Future studies involving more universities providing occupational therapy education can be planned to increase the representativeness of study results. Another limitation of the study is the absence of a control group (e.g., students attending face-to-face education during the COVID-19 period). The study was voluntary. Self-reports and study findings may be influenced by social desirability response bias (Latkin et al., 2017). Despite the usefulness of questionnaires in measuring perceptions and attitudes, qualitative research is more appropriate for trying to understand the meaning people give to the topic of interest in their context (Selva Olid et al., 2012). In this context, the fact that only questionnaires were used in the study can be considered as a limitation. In future research, different research methods (observational, mixed, etc.) can be used to provide more information about students’ learning, attention, and motivation. Despite all the limitations, it is thought that this subject will be the first study in this context in literature, and it will be a guide for future studies. Considering that the study was conducted in a university that prevails hybrid education, carrying out the PLS validity and reliability study with both face-to-face and online learning students is a strength of the study.

Conclusion

This study was planned to examine the predictors of perceived learning in occupational therapy students in terms of different variables in the hybrid education process. Our results showed that perceived learning and academic motivation are interrelated concepts in occupational therapy students. In addition, the most important predictor of perceived learning is amotivation. To conclude, we suggest that developing intrinsic and extrinsic motivation and reducing amotivation in students studying at hybrid universities can be used as a strategy to increase attentional control and perceived learning. It was shown that although extrinsic motivation was linked to a superficial approach to studying, intrinsic motivation was positively connected with productive approaches to learning (Walker et al., 2006). Therefore, it can be assumed that individuals with higher levels of intrinsic motivation are also more likely to engage in positive behaviors for the study curriculum, reflecting the need for greater validation of productive approaches to studying in curriculum plans for occupational therapy students. Nevertheless, since extrinsic motivation makes the student dependent on a motivation source other than himself/herself, and the risk of decreasing the positive behaviors expected from the students with the disappearance of the extrinsic motivation sources, it is important to develop strategies for making interventions for more sustainable intrinsic motivation and using extrinsic motivation resources to increase intrinsic motivation.

The result obtained from the current study explains that amotivation is the most important variable on learning from perception, which draws attention to the fact that the concept of amotivation should be examined in terms of different variables and should be taken into account in perceived learning, apart from the subject of internal and external motivation, which one draws more attention in the literature. It was determined that the learning processes of students in the field of health were negatively affected during the COVID-19 period, when different types of e-learning education were transitioned (Meo et al., 2020; Ye et al., 2020). Active learning strategies can be implemented in online sessions aimed at higher-order thinking (e.g., analysis and synthesis) that enables students to assimilate, apply, and maintain learning. The implementation of active learning strategies also takes into account the various learning styles of the students, supports the student’s success, and strengthens their motivation (Austin and Mescia, 2004). It has been shown in the literature that components of clinical skills can be integrated into digital learning in occupational therapy education. The digital learning designs applied to occupational therapy education enable knowledge and skill acquisition, improvement of learning outcomes, participation in learning, and reflection. In addition, it improves cooperation between students. In addition, various technologies used in digital learning can facilitate active learning and increase motivation by providing learning strategies such as synchronous or asynchronous thinking and reflection, discussion, and peer learning group activity (Hwang et al., 2023).

The results of this study show that occupational therapy students’ motivation and attention control have an effect on their perceived learning, and amotivation is the most influential factor examined on perceived learning. In addition, in hybrid education, in order to increase students’ attention and motivation, arousing interest in learning, improving educational experience, improving the quality of results, and the level of support students receive from their institutions can enable them to reach a high-quality occupational therapy education standard (Hollis and Madill, 2006). In addition, these factors, which are thought to improve students’ attention and motivation, can be examined in detail if educators are prepared to communicate more with students about the learning environment created in higher education, learning modes that can suit students’ life demands, appropriate preparation, and support for learning.

In future studies, it is important to compare the subject of learning perception in terms of online, hybrid, and face-to-face education. Another issue is that face-to-face and online course rates should be evaluated in universities that implement hybrid education in different ways. In particular, the difficulties and deficiencies of occupational therapy students who have applied for courses and internships in the hybrid education process should be determined. Effectively supporting the process, directing it, and managing it well are important in terms of post-graduate work performance.

Key findings

Perceived learning and academic motivation are interrelated concepts in occupational therapy students.

In the hybrid education process of occupational therapy students, the most important predictor of perceived learning is amotivation.

The difficulties and deficiencies of occupational therapy students who have used courses and internships in the hybrid education process should be identified in particular.

What the study has added

This study is the first to provide information about hybrid education in occupational therapy students. We suggest that improving the intrinsic and extrinsic motivation and reducing amotivation in students studying at universities offering hybrid learning can be used as a strategy that increases attentional control and perceived learning.

Footnotes

Research ethics

All procedures performed in the study involving human participants were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Patient and public involvement data

During the development, progress, and reporting of the submitted research, patient and public involvement in the research was not included at any stage of the research.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors declared no financial support for the research, authorship, and/or publication of this article.

Contributorship

Study design and conceptualization Öİ; data collection and carrying out the study Öİ, EÖ, AG; interpretation of study findings Öİ, EÖ, AG; writing the article Öİ, EÖ, AG; critical review Öİ, EÖ, AG.

ORCID iDs

Özgü İnal

Esma Özkan

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