Gender and circadian preferences influence emotions and motivation in secondary mathematics classrooms

ABSTRACT

Many studies have reported poor school achievement in evening persons and general circadian fluctuations in cognition. The aim of this study was to analyze circadian fluctuations in a cross-sectional design and examine the effects of chronotype on situational emotions and intrinsic motivation. A cross-sectional survey study was conducted in three Turkish secondary schools with a total sample of 599 students (283 females and 316 males). Data were collected at the end of specific math lessons of the same grade level and content, using a form combining three scales. We found no gender-related differences in intrinsic motivation, while there were some differences in situational motivation. In math classes, female students exhibited higher level of interest, while boys scored higher on boredom. In addition, students who scored high on morning affect reported higher levels of interest, well-being, and less boredom. Students with higher stability (and lower fluctuations in mood and cognition during the day) reported a higher degree of enjoyment, perceived competence, perceived choice, and less pressure/tension in their math lessons. A positive association was observed between distinctness, interest, and well-being, while negative correlations existed between distinctness and boredom. This suggests that students with higher diurnal stability reported a higher level of interest, well-being, and a lower level of boredom. Additionally, the results of the analyses showed that morningness, distinctness, and eveningness were significant predictors of intrinsic motivation. Conversely, gender, time of application, morningness, and distinctness emerged as predictors for situational emotions in mathematics classes.

KEYWORDS:

• Circadian preference
• gender
• situational emotions
• intrinsic motivation
• mathematics

Introduction

Morningness-eveningness, chronotype, or circadian preference are related to the timing of sleeping and our “feeling-best” times during the day. Morning people get up and go to bed early, while evening people tend to get up and go to bed later (Adan et al. 2012). This can be assessed by a range of questionnaires, with the Morningness-Eveningness-Stability-Scale improved (MESSi; Randler Diaz-Morales et al. 2016) being the most recentl development (see below for details). During the last two decades researchers in this field have found that circadian preference has a significant influence on academic achievement both at the school and university levels (Tonetti et al. 2015), with morning people performing significantly and consistently better. Although other aspects of academic achievement have been studied in combination with circadian preference such as intelligence, motivation, and the need for cognition (Arbabi et al. 2015; Preckel et al. 2013; Rahafar et al. 2017), the relationship between achievement-related situational emotions and situational motivation has not yet been examined. This is surprising, as emotions appear to play an important role in learning and instruction (Gläser-Zikuda et al. 2005). In comparison to general, personality-like traits, situational emotions and motivation fluctuate and can be considered state-like expressions.

Morningness-eveningness

The biological rhythm of human beings follows a 24-hour rhythm that is controlled by an internal clock (Adan et al. 2012). However, the internal clock has a longer cycle than the 24-hour day and is regularly reset to 24 hours by external zeitgebers such as daylight. Within this rhythm there are fluctuations in body temperature, hormones, and affect (Baehr et al. 2000; Murray et al. 2009). Despite the general trends in circadian rhythms, there are considerable individual differences. Morningness-eveningness is a biological trait which is related to specific genetic traits (Archer et al. 2003; Jones et al. 2016) and is normally distributed in the population. Thus, the preference for a given time to work or sleep differs between individuals. However, during their lifespans, people change their sleep-wake rhythms and circadian preference from being morning-oriented in childhood to a drastic change to evening-orientation during adolescence. Later, there is a somewhat slower change towards morningness again during the 20s and 30s. At the age of 70 most people seem to return to being early morning types (i.e., like toddlers) again (see Randler et al. 2014, 2016, 2019; Roenneberg et al. 2004). Age, gender (see below), and measurement tool application time are significant variables for chronotype (Randler et al. 2014). In this respect, they were included in the study.

Situational emotions

In the early 1990s, Pintrich et al. (1993) argued that learning processes involve more than just “cold cognition,” and advocated the inclusion of additional factors such as affective and social variables. Such affective variables (e.g., emotions) and learning achievement are related in such a way that positive emotions such as interest and well-being have a positive impact on achievement, while negative emotions such as boredom, anger, anxiety, or disgust have a negative one (Allen 2010; Randler et al. 2012). Using Gläser-Zikuda et al. (2005) and Pekrun et al. (2011) definitions as a basis, in the present study we define emotions as achievement-related emotions in a situational (state) concept. Situational emotions are based on the fluctuations of emotions according to different lessons or different times of the day. This concept is distinguished from trait-aspects that represent an enduring, personality-like variable (For an example to illustrate the state-trait-concept, see e.g., Spielberger et al. (1970) State-Trait-Anxiety Inventory). The idea behind the state-trait distinction regarding learning emotions can be clarified by the following example: Pupils may perceive a particular lesson such as an experimental lesson in biology or a specific topic (e.g., animals) as being interesting even though they do not have a general interest in biology (see Gläser-Zikuda et al. 2005). In this study, we follow Randler et al. (2011) and define situational emotions as emotions that are sensitive to changes and that are not developed as a stable trait factor (e,g., a general interest in a specific topic). This is a construct found in many psychological variables where state and trait components exist simultaneously (Spielberger et al. 1970).

In line with the previous work of Randler et al. (2011), we also use the emotions interest, well-being, and boredom. According to Gläser-Zikuda et al. (2005), interest is understood as a cognitive-emotional construct and in this context is also treated as an emotion (in contrast to the “achievement emotions” of Pekrun et al. (2011) where interest is not taken into account).

Intrinsic motivation

The study of motivational aspects is based on Deci and Ryan’s (2000) and Ryan and Deci (2017, 2019) self-determination theory (SDT). In this study, we focused on the intrinsic motivational dimensions of SDT in particular. These are based on a set of three innate psychological needs (Deci and Ryan 2000): relatedness, competence, and autonomy. In SDT, Ryan and Deci (2017) state that the satisfaction of these basic psychological needs plays an essential role in fostering the positive qualities of motivation and learning (Deci and Ryan 2000; Eckes et al. 2018; Hofferber et al. 2016; 2019; Randler et al. 2012; Ryan and Deci 2017). SDT proposes that there are two general types of motivated behaviors: those that are consciously chosen in the service of intrinsic or extrinsic needs – in other words, the self-determined behaviors; and those that are not consciously chosen – in other words, “mindless” or automated behaviors that require less involvement of the higher cerebral functions (Deci and Ryan 2012, 33). In other words, the basis of our conscious behavior is our internal and external motivations.

SDT highlights the importance of intrinsic motivation and the ways in which social contextual features facilitate the emergence of intrinsic motivation to engage in a task (Eccles and Wang 2012). The satisfaction of basic psychological needs plays an essential role in fostering intrinsic motivation (Kalajas-Tilga et al. 2020), and then intrinsic motivation can foster learning (based on the SDT, it is rather a mediated relation). While SDT can generally be considered a personality-like trait expression of motivation, there are still some fluctuations. To account for this, Wilde et al. (2009) developed a questionnaire based on SDT with the four dimensions interest/enjoyment, pressure/tension, perceived competence, and perceived choice. Their questionnaire represents a short scale of intrinsic motivation that is an adapted, time-efficient version of Ryan’s (1982) Intrinsic Motivation Inventory (IMI). Wilde et al. (2009) were able to demonstrate that this short scale can detect differences in motivation during different lessons and is therefore sensitive to changes.

Circadian variations and individual difference in emotions and mood

Some studies have already addressed diurnal fluctuations of emotions, especially Positive Affect (PA; Murray et al. 2009). In general, PA follows a sinusoidal curve, that is, it is even less pronounced in the early morning, rises during the course of the day and reaches its peak towards late afternoon/evening, only to drop again afterwards. The nadir is reached around 3:00 and 4:00 at night/in the morning. Randler and Weber (2015) were able to identify this by comparing pupils during their first and last lessons of the day based on the Positive and Negative Affect Schedule (PANAS; Watson et al. 1988). They found that PA increased during the school day and reached its peak in the sixth lesson (Randler and Weber 2015). Regarding different circadian preferences, Randler, Rahafar et al. (2014), using a list of adjectives, found that morning type students showed significantly more positive mode during the first lesson of a school day. In a follow-up study, Randler and Weber (2015) examined PA in the first and sixth lessons and reported that morning people already had a higher PA than evening people during the first lesson. Interestingly, the morning people in the sixth lesson still scored higher than the evening people in terms of positive mood. Based on a single item (mood rating), Díaz-Morales et al. (2015)) measured the current level of pleasantness three times during the school day (at about 8:00, 11:00, and 14:00). Mood increased throughout the school day from the lowest levels in the morning. Concurrent with Randler Rahafar et al. (2014) morning types showed better mood compared to other chronotypes. Itzek-Greulich et al. (2016) studied adolescents that either participated in a morning (start time: 9:00) or an afternoon course (start time: 15:00). Data were gathered on achievement (starch chemistry, pre and post) and on state motivation. The results indicated a synchrony effect (interaction of time of day and chronotype). Evening types expressed lower levels of interest and joy in the morning. Moreover, no significant effect of chronotype had been found in the afternoon, suggesting that all students benefited from afternoon lessons.

Gender differences

Gender differences are manifold. Concerning chronotype, there are considerable gender differences in morningness-eveningness and chronotype. Women and girls are usually more morning-oriented, at least during puberty and until menopause (Randler and Engelke 2019; Roenneberg et al. 2004). However, during childhood and the start of the puberty, boys and girls do not differ in chronotype (Randler et al. 2017; Roenneberg et al. 2004). Similarly, gender differences decrease again around the age of about 50 years, which is around menopause (Randler and Bausback 2010). These differences have been linked with sex hormones (Randler et al. 2017).

Considering school achievement, a meta-analysis reported a small but significant female advantage in general effect sizes, which is largest in language and smallest in math (Voyer and Voyer 2014). To explain gender differences in achievement, different predictors have been tested in another meta-analysis (Spinath et al. 2014). Spinath et al. (2014) reported that gender differences in students’ individual characteristics varied from non-existent (e.g. general intelligence) to strong (e.g. self-discipline) and gender differences in intelligence, personality and motivation partially mediated the association between gender and school achievement. In detail, boys have a more positive ability self-concept in mathematics and girls in languages, the same was found for interest. With respect to mathematics, Frenzel et al. (2007) reported that boys and girls did not differ in achievement, but girls reported significant lower interest/enjoyment and more anxiety.

Current study

The current exploratory study brings together these hitherto unrelated research areas. The aim of this study was to analyze circadian fluctuations in a cross-sectional design and chronotype effects on situational emotions and intrinsic motivation. The following questions will be answered in the research:

1. Does the gender variable predict enjoyment, perceived competence, perceived choice and pressure/tension when MA, DI, EV, application time, and age variables are controlled?

2. Does the gender variable predict interest, well-being, and boredom when MA, DI, EV, application time, and age variables are controlled?

3. Is there an influence of the time of day on situational learning emotions and motivation?

4. Are there bivariate correlations between the different sleep-wake and multi-dimensional morningness measures and situational learning emotions and motivation?

Materials and methods

Participants and data collection

After obtaining ethical permission for the research, identifying information about the participants (name, number, school, etc.) was not collected during the data collection process. Permission was obtained from the Directorate of National Education for the implementation of the questionnaires the questionnaires were in agreement with the Declaration of Helsinki (2001). Previously developed and ethically approved measurement tools were used in the study. Before the data collection begun it was carried out with the approval numbered 24347576-199-E.25795003 from the district national education directorate on 25 December 2019.

In this study, a cross-sectional survey model was applied. The participants were randomly sampled from high school students in Turkey. Printed forms of questionnaires were distributed to randomly chosen mathematics teachers at each school. Then these forms were filled out by students under the teachers’ supervision at the end of a mathematics lesson. The end of the lesson was preferred because of the scale items that were related to the immediate situation and that referred to the lesson that had just taken place. This eliminated the need to think retrospectively and encouraged providing instant answers. It took 10 minutes for the students to fill out the survey forms. In addition, all data were collected within the same week when the same topics were processed to minimize the difficulty of the topics affecting the students’ preferences. Light input and lighting conditions can substantially influence related variables of motivations, boredom and alertness (Hull et al. 2003; Lok et al. 2022). Since the data collection process was completed within a week, light conditions as confounders were excluded in the study. Similar studies can be carried out in future studies by controlling light conditions or considering their changes. Thus, this study benefits from sampling the same intellectual content of a mathematics lesson in a standardized manner. These schools start education at around 9.00 in the morning and finish at 16.00 in the evening. A total of 623 questionnaires were distributed and data were obtained from 599 participants. Since the answers given by 24 participants were missing, they were not included in the analysis. Of the students participating in the study, 316 were male, 283 were female. The mean age was 15.75 (SD = 1.03) and ranged from 13 to 18. The data were collected at different times of the day between 9 a.m. and 4 p.m. to cover a broad range of clock times. Implementation times are presented in Table 1.

Table 1. Data collection time table.

Time	N	%	Time	N	%	Time	N	%	Time	N	%	Time	N	%
09:00	30	5.0	10:45	15	2.5	12:00	47	7.8	14:30	116	19.4	15:30	43	7.2
09:10	26	4.3	10:50	15	2.5	13:30	19	3.2	14:40	13	2.2	15:45	25	4.2
09:40	25	4.2	11:15	33	5.5	13:35	17	2.8	14:50	16	2.7	16:00	34	5.7
09:45	24	4.0	11:40	48	8.0	14:00	28	4.7	15:15	25	4.2

Measurement instruments

The Morningness–Eveningness-Stability-Scale

To measure morningness – eveningness, we used the Morningness – Eveningness-Stability-Scale (MESSi) developed by Randler et al. (2016). In order to implement it in a Turkish context, we used Demirhan et al.’s (2019) adapted version thereof. The final version of the scale consisted of 15 Likert items and three distinct dimensions: morning affect (MA), distinctness (DI), and eveningness (EV). The Cronbach’s alpha values of the respective dimensions were .76 for MA, .66 for DI, and .72 for EV. In this analysis, high scores on MA represent a higher degree of morning affect, higher scores on EV constitute a higher degree of evening affect, and high scores on DI amount to lower fluctuation, which means higher stability.

The Situational Emotions Scale (SE)

The Situational Emotions Scale (SE) was originally developed by Randler et al. (2011) in the German language. We used Horzum et al.'s (2020) Turkish version in our study. The scale consisted of three factors to measure emotions during instruction – interest, well-being, and boredom – with each one having three items. Cronbach’s alpha for each factor amounted to .71 for interest, .84 for well-being, and .69 for boredom.

The short scale of Intrinsic Motivation Inventory (IMI)

The short scale of intrinsic motivation is a time-economic version of Ryan’s (1982) Intrinsic Motivation Inventory (IMI). It was adapted and translated into German by Wilde et al. (2009). For the present study the short version of IMI was, in turn, adapted and translated into Turkish by Duman et al. (2020). Our scale consisted of four factors: interest/enjoyment, perceived competence, perceived choice, and pressure/tension. Each factor had three items. Cronbach’s alpha was .75 for interest/enjoyment, .82 for perceived competence, .74 for perceived choice, and .75 for pressure/tension.

Other variables

Gender, grade, and application time were included as other variables. Also, habitual sleep duration was asked for free days and weekdays. One question was added to the questionnaire for each of these variables.

Statistical analysis

The data were collected as a paper and pencil test and entered into the package program. We used correlations to assess the bivariate relationships between each of the variables and subscales. In addition, general linear modelling was used. SPSS 21 (IBM, Somers, NY) was used to analyses the data and the alpha level for significance was set at .05. Situational emotions and intrinsic motivation were used as dependent variables. Thus, two separate multivariate general linear models were applied as dependent variables. While morning affect, distinctness, eveningness, application time, and age were used as covariates, gender was used as the independent variable.

Results

Firstly, descriptive statistics were analyzed and are presented in Table 2.

Table 2. Descriptive statistics of the sample.

Variable	Range	Mean	SD	Skewness	Kurtosis
Age	13–18	15.75	1.03	0.03	−0.61
Application time	9:00–16:00	12:59	2:15	−0.39	0.23
Morning Affect (MA)	5–25	14.93	4.22	−0.25	−0.39
Distinctness (DI)	5–25	14.65	3.20	0.01	0.34
Eveningness (EV)	5–25	17.80	4.19	−0.34	−0.26
Interest enjoyment (IMI)	1–5	3.16	0.98	−0.31	−0.34
Perceived competence (IMI)	1–5	3.31	1.03	−0.33	−0.35
Perceived choice (IMI)	1–5	3.22	0.87	−0.30	0.11
Pressure/tension (IMI)	1–5	2.02	0.98	0.64	0.27
Interest (SE)	1–5	3.68	0.95	−0.64	−0.10
Well-being (SE)	1–5	3.64	1.02	−0.63	−0.07
Boredom (SE)	1–5	2.85	1.00	0.13	−0.76

The participants’ MA, DI, and EV scores ranged from 5 to 25, with the EV score being the highest. Their MA and DI scores were lower than their EV score. Furthermore, the IMI dimensions interest/ enjoyment, perceived competence, perceived choice, and pressure/tension as well as the SE dimensions interest, well-being and boredom scores ranged from 1 (low) to 5 (high). The lowest scores on the IMI scale dimensions were found for pressure tension while perceived competence scored highest. When it came to the SE scale dimensions, boredom scored lowest and interest highest.

Boys and girls differed in EV, DI, sleep duration on free days and perceived competence (Table 3). Boys showed a higher eveningness, lower fluctuations during the day, longer sleep duration on free days, and a lower perceived competence (Table 3).

Table 3. Comparison of boys and girls in the different study variables.

	Male	(N = 316)	Female	(N = 283)
	Mean	SD	Mean	SD	T	p
Age	15.73	1.05	15.78	1.00	−0.591	0.554
Application time	13:03	02:12	12:54	02:19	0.786	0.432
MA (Morning Affect)	14.68	3.98	15.21	4.46	−1.542	0.124
EV (Eveningness)	17.34	4.30	18.32	4.01	−2.873	0.004
DI (Distinctness)	16.14	2.99	14.47	3.20	6.571	<0.001
Sleep duration free days	10:01	01:36	09:10	01:58	5.797	<0.001
Sleep duration school days	08:12	01:03	08:04	01:10	1.368	0.172
Interest enjoyment (IMI)	3.14	0.95	3.19	1.02	−0.740	0.460
Perceived competence (IMI)	3.22	1.02	3.41	1.04	−2.335	0.020
Perceived choice (IMI)	3.16	0.85	3.28	0.89	−1.737	0.083
Pressure tension (IMI)	2.01	1.02	2.03	0.94	−0.316	0.752
Interest (SE)	3.75	0.93	3.60	0.96	1.946	0.052
Well-being (SE)	3.63	1.00	3.65	1.04	−0.340	0.734
Boredom (SE)	2.83	0.98	2.87	1.03	−0.507	0.612

Pearson’s correlations were used to assess bivariate relationships among the dimension scores on the IMI and SE scales and the scores for application time, age, morning affect, distinctness, and eveningness (see Table 4).

Table 4. Correlations between the intrinsic motivation inventory (IMI) and situational emotions (SE) dimensions and morning affect (MA), distinctness (DI), eveningness (EV), sleep duration on weekdays (SD), application time (AT), and age.

	IMI				SE
	Enjoyment	Perceived Competence	Perceived Choice	Pressure/ Tension	Interest	Well-being	Boredom
MA	.275**	.192**	.069	−.164**	.180**	.289**	−.367**
DI	.235**	.230**	.100*	−.186**	.213**	.211**	−.278**
EV	.040	.003	.064	−.096*	.045	−.030	.051
SD	.109**	.032	.021	.003	.070	.047	−.101*
AT	−.052	−.066	−.009	.006	−.003	−.086*	.114**
Age	−.054	.034	.085*	.022	−.086*	−.047	.066

*denotes p < .05, ** denotes p < .001.

As a result of the correlation analyses, it was found that there were statistically significant relationships between some variables and insignificant relationships between others. When the r values of the variables with significant relationships are analyzed, it is seen that all relationships are at a low level. We consider correlation coefficients about 0.1 as small, above 0.2 as typical and meaningful, and above 0.3 as relatively large (Gignac and Szodorai 2016). When analyzed on a variable-by-variable basis, the following findings were revealed.

A significant positive association was found between MA and IMI scale enjoyment (r = .275, p < 0.01) and perceived competence (r = .192; p < 0.01). Significant negative correlations existed between MA and pressure tension (r = −.164, p < 0.01). This suggests that MA students reported higher levels of enjoyment and perceived competence as well as lower pressure/tension scores regarding their mathematics lesson. However, MA was unrelated to the perceived choice scores. Additionally, a significant positive association between MA and the SE scale factor interest (r = .180, p < 0.01) and well-being (r = .289; p < 0.01) was found. Negative correlations were significant between MA and boredom (r = −.164, p < 0.01). This suggests that MA students reported high levels of interest and well-being and low boredom scores regarding their mathematics lessons.

Similarly, significant positive correlations were found between DI and IMI scale factors enjoyment (r = .235, p < 0.01), perceived competence (r = .230; p < 0.01), and perceived choice (r = .100, p < 0.05). Furthermore, there were significant negative correlations between DI and pressure/tension (r = −.186, p < 0.01). This suggests that students with a higher level of stability (and fewer fluctuations during the day) reported high degrees of enjoyment, perceived competence, perceived choice, and lower pressure/tension scores in mathematics lessons. Furthermore, it was found a significant positive association between DI and SE scale interest (r = .213, p < 0.01) and wellbeing (r = .211; p < 0.01). The negative correlations between DI and boredom were significant (r = −.278, p < 0.01). This suggests that students with a higher diurnal stability reported higher levels of interest, well-being and lower boredom scores concerning their mathematics lessons.

By contrast, EV was unrelated to the IMI scale factors enjoyment (r = .040, p > 0.05), perceived competence (r = .003; p > 0.05), and perceived choice (r = .003; p > 0.05). However, EV showed a significant negative association with pressure/tension. Similarly, EV was also found to be unrelated to the SE scale factors interest (r = .045, p > 0.05), well-being (r = −.030; p > 0.05) and boredom (r = .051; p > 0.05). This suggests that participants’ EV scores were not significantly related to intrinsic motivation and situational emotions scores in their mathematics lessons. Generally, these results show that morning-oriented and DI students with more stability have a higher level of intrinsic motivation and a more positive emotional state. They also show that different manifestations of EV showed the same level of motivation and emotion.

Sleep duration on weekdays correlated positively with enjoyment and negatively with boredom. Thus, longer sleepers experienced more enjoyment and less boredom.

To analyze the different variables simultaneously, we used two separate multivariate general linear models. First, a multivariate general linear model was used to assess the effects of gender on the intrinsic motivation scale dimensions with the covariate variables MA, DI, EV, application time, and age. There was a significant influence of the MA (Pillai’s trace = .057, F_4–587 = 8.943, p < 0.001, η² = .057), DI (Pillai’s trace = .045, F_4–587 = 6.984, p < 0.001, η² = .045), and EV (Pillai’s trace = .022, F_4–587 = 3.307, p < 0.05, η² = .022) scores on intrinsic motivation scale dimensions with a substantial effect size. All other variables revealed no effect in the multivariate model. Subsequent univariate analyses were carried out (Table 5). No significant difference was found in the four sub-dimensions of intrinsic motivation scale in terms of gender.

Table 5. General linear multivariate model assesses the effects of gender on the enjoyment, perceived competence, perceived choice and pressure/tension with the covariate variables MA (Morning Affect), DI (Distinctness), EV (Eveningness), application time, and age.

Independent	Dependent	df	F	p	η^2
Gender	Enjoyment	1	.696	0.404	.001
	Perceived competence	1	.883	0.348	.001
	Perceived choice	1	1.078	0.300	.002
	Pressure/tension	1	3.195	0.074	.005
Age	Enjoyment	1	.464	0.496	.001
	Perceived competence	1	1.852	0.174	.003
	Perceived choice	1	5.300	0.022	.009
	Pressure tension	1	.000	0.984	.000
Application time	Enjoyment	1	1.327	0.250	.002
	Perceived competence	1	2.177	0.141	.004
	Perceived choice	1	.010	0.922	.000
	Pressure/tension	1	.020	0.889	.000
MA	Enjoyment	1	28.358	<0.001	.046
	Perceived competence	1	9.126	0.003	.015
	Perceived choice	1	1.569	0.211	.003
	Pressure/tension	1	8.521	0.004	.014
DI	Enjoyment	1	12.225	0.001	.020
	Perceived competence	1	15.174	<0.001	.025
	Perceived choice	1	2.344	0.126	.004
	Pressure/tension	1	11.467	0.001	.019
EV	Enjoyment	1	2.145	0.144	.004
	Perceived competence	1	.000	0.984	.000
	Perceived choice	1	2.251	0.134	.004
	Pressure/tension	1	7.383	0.007	.012

In bold: significant values.

A second multivariate general linear model was used to assess the effects of gender on situational emotions with MA, DI, EV, application time, and age as covariates. We found a significant influence of gender (Pillai’s trace = .030, F_3–588 = 6.123, p < 0.001, η² = .030), application time (Pillai’s trace = .024, F_3–588 = 4.810, p < 0.005, η² = .024), MA (Pillai’s trace = .096, F_3–588 = 20.805, p < 0.001, η² = .096), and DI (Pillai’s trace = .048, F_3–588 = 9.975, p < 0.001, η² = .048) on the situational emotions scale dimensions with a substantial effect size. All other variables revealed no effect in the multivariate model. Subsequent univariate analyses were carried out (Table 6).

Table 6. General linear multivariate model assesses the effects of gender on interest, well-being, and boredom with the covariate variables MA, DI, EV, application time, and age.

Independent	Dependent	df	F	p	η^2
Gender	Interest	1	13.020	0.000	.022
	Well-being	1	.857	0.355	.001
	Boredom	1	5.579	0.019	.009
Age	Interest	1	2.473	0.116	.004
	Well-being	1	.274	0.601	.000
	Boredom	1	.641	0.424	.001
Application time	Interest	1	.006	0.938	.000
	Well-being	1	4.786	0.029	.008
	Boredom	1	9.644	0.002	.016
MA	Interest	1	7.720	0.006	.013
	Well-being	1	32.609	<0.001	.052
	Boredom	1	54.501	<0.001	.085
DI	Interest	1	20.559	<0.001	.034
	Well-being	1	8.512	0.004	.014
	Boredom	1	19.892	<0.001	.033
EV	Interest	1	2.487	0.115	.004
	Well-being	1	.081	0.776	.000
	Boredom	1	.436	0.509	.001

In bold: significant values.

A gender effect emerged regarding the interest scores with the girls scoring higher on interest (estimated marginal means ± SE from the GLM: girls: 3.75 ± .93; boys: 3.59 ± .96). However, another gender effect emerged when it came to the boredom scores with the boys scoring higher on boredom (estimated marginal means ± SE from the GLM: girls: 2.83 ± .98; boys: 2.88 ± 1.03). Still, despite the two statistical significances, the effect sizes were low.

Discussion

There were no significant gender effects concerning motivation in mathematics in the linear models. As for emotions, the girls showed a higher degree of interest and lower degrees of boredom. Based on trait measurements, boys usually show higher interest in mathematics (Frenzel et al. 2010). However, these studies are normally based on general trait or domain specific questions rather than on situational measures as in our study. We thus encourage educational research to evolve away from the simple assessments of trait-interests to more situational measures. Perceived competence in mathematics lesson was higher in girls compared to boys. This is in line with some studies about motivation (for Turkey: Süren and Kandemir 2020). Concerning sleep variables, boys scored higher on EV and slept longer on free days. The first result is well in line with many previous studies that boys and men are more evening oriented and show longer sleep duration (Randler et al. 2017, 2019). Thus, it was meaningful keeping gender as a predictor variable in the subsequent linear models.

The achievement emotions well-being and boredom changed significantly during the day, while there was no influence on interest. This study is the first to assess achievement-related learning emotions during a typical school day in a given subject (mathematics). Previous research such as Randler and Weber’s (2015) work using the Positive and Negative Affect Schedule (PANAS) has already shown that learners exhibit differences in emotions during the day. The current level of pleasantness increases during the school day (Díaz-Morales et al. 2015). However, in our study, we also controlled for the content of the lesson. Moreover, all lessons were taught in mathematics to avoid any interference from the subject itself. For example, when biology is taught in the first lesson and sports in the last lesson, then higher positive emotions may only arise because sport lessons may be generally rated better than biology.

Our research has revealed some significant differences to the research conducted thus far. For instance, application time had a significant effect on well-being and boredom. These results, however, were contradictory to previous work because well-being decreased while boredom increased throughout the course of the day. Previous studies found that both positive affect and pleasantness increased over time. This difference might occur because the emotions in the current study were related to learning achievement. There are two processes that regulate sleep and wakefulness (Borbély 1982), with a circadian effect (fluctuations during the day) and an increasing homeostatic sleep pressure which starts after awakening. Comparable to our study on emotions, a similar effect was found in standardized test scores (Sievertsen et al. 2016) in which the test scores decreased as the day progressed. Another interesting question might be whether these fatigue effects may be subject-specific, that is, whether they occur in all subjects in a similar manner or whether there are between-subject differences. As mathematics is a very mentally demanding subject, the effects may be lower or inverted in subjects such as arts, music, or sports.

Interestingly, we found no time-of-day effect on motivation as measured with a short version of the IMI (Wilde et al. 2009). There are several possible reasons for this. First, although motivation is sensitive to changes (at least when it comes to this measurement instrument), it may not have the same degree of high amplitude that emotions or affect has, thus making it difficult – if not impossible – to detect circadian fluctuations. This would mean that motivation is a more stable construct than learning emotions. This would be an interesting aspect to address in further research. Another explanation for the lack of time-of-day effect on motivation could be due to the fact that changes in motivation may only be so small the questionnaire might not be able to detect such changes. Another point is that the measurement tools are different. When measurement tools are different from each other, they address the quality they measure with different theoretical perspectives, dimensions and items. This is an expected situation as it causes them to produce findings of different quality. The situational emotions (Gläser-Zikuda et al. 2005) may be more sensitive to changes than the motivational construct of the IMI (Wilde et al. 2009). Finally, it is possible that mathematics may be a subject in which motivation may be more constant during the day. This should be assessed with future studies in other subjects.

Morningness was associated with higher levels of enjoyment and perceived competence and a lower level of pressure/tension. Concerning emotions, morningness correlated positively with interest and well-being and negatively with boredom. Although this is a new result, it appears to be in line with previous work in which morning-oriented students showed higher degrees of positive affect and pleasantness (Díaz-Morales et al. 2015; Randler and Weber 2015). Similarly, many studies have found that morning people perform better both in school and at university (for an overview, see Tonetti et al. 2015). Our results provide additional support for the hypothesis that morningness and school achievement are related in many ways. For example, since achievement and emotions have been found to be closely related (see e.g., Gläser-Zikuda et al. 2005), positive emotions and higher motivation may be linked to better school achievement. Nevertheless, we do not know the underlying cause and effects of the emotion-achievement relationship because early school start times better suit morning people, which, in turn, may improve their well-being, interest, and motivational factors that, one again, may improve achievement scores.

Eveningness was unrelated to most of the constructs with the exemption of pressure/tension. This is an interesting aspect because it shows that eveningness and morningness can be interpreted as two different dimensions rather than two ends of a continuous scale (see discussion in Randler et al. 2016).

As another finding, distinctness or stability turned out to be related to motivational factors (enjoyment, competence, and choice) as well as emotional aspects (higher interest, well-being, and less boredom). This suggests that low circadian fluctuations (and higher stability) are related to positive emotions and motivation. These results are somewhat related to studies that found that people (adults) with higher fluctuations during the day are more prone to psychological distress. For example, high fluctuations were related lower scores in general health (Díaz-Morales et al. 2017).

The fact that longer sleepers experienced more enjoyment may be related to earlier sleep onset rather than to sleep phase, and an earlier sleep onset may relate not only to chronotype, but habitual duration and timing of outdoor light exposure. This aspect deserves further attention because it would be beneficial for sleep hygiene and interventions when students are exposed to outdoor light, which may shift circadian phase and sleep onset, and in consequence leads to longer sleep duration.

Suggestions for further research

Among the variables under control, application time, morningness, distinctness, and eveningness have produced significant effects. In this respect, follow-up studies should use the time of application, morningness, distinctness, and eveningness as independent variables. From these, morningness and distinctness seem to be the most relevant factors.

In addition, the relationship between similar variables and other emotional and cognitive variables such as satisfaction, willingness, and perceived learning can be examined as dependent variables in future studies. Some previous studies have pointed out that distance education and technology supported applications have considerable effects on morningness and eveningness (Horzum et al. 2014; Randler et al. 2014), because there is some variance during the day when students do their tasks (Staller et al. 2022). In this respect, a similar study can be conducted with distance education students with similar variables.

Limitations

One of the limitations of our study is that the data were collected from students studying at secondary schools and taking mathematics lessons. Moreover, the data were collected from math’s lessons and all data were collected during the same week in which the same topics were processed to minimize the difficulty of the topics and thereby minimize any effects on the students’ preferences. However, the participants’ mathematics achievement levels were not taken into consideration. This aspect should be addressed because motivation differences are related to both chronotype and gender, and morningness is generally more common in adolescent girls than in boys. As emotions and motivation can depend on school achievements also in some kind of a feedback-loop, longitudinal studies may have a look at covariance and changes of chronotype and motivation, in combination with school achievement.

Future studies could address student achievement by giving performance-based tasks that can be offered at different times. Future studies could also compare performance as well. Anxiety was another factor on which we did not sufficiently focus. Mathematics lessons can cause anxiety for some students. Therefore, anxiety variables (such as state-trait anxiety, see above) can be addressed in future studies. It would also be beneficial to examine whether there is a similar situation in different courses with other topics in mathematics.

Another aspect we could not control for was the role the teacher may have played in influencing their learners. For example, he/she may have detected fatigue in his/her students and adapted his/her teaching accordingly. Different teachers may have created a different effect. In this respect, in future studies, this application can be applied to the groups of the same teacher or necessary measures should be taken to ensure that teachers teach the same subject in a similar way. If it is desired to collect data from large groups, the teacher effect can be eliminated by presenting the content asynchronously online.

Moreover, the biological rhythm and motivation of the teacher was not taken into consideration. Finally, another limitation was the cross-sectional survey method we used in the study. Future studies in which the dependent variables are measured more than once can be performed in accordance with the longitudinal survey model. In addition, studies can be conducted to support the findings with qualitative data.

Conclusion

As a conclusion of the study, we found that morningness, distinctness, and eveningness are significant predictive variables for intrinsic motivation. As achievement emotions and motivation are tightly related to achievement, we suppose that morning students are on a double-advantage: they perform better in the morning and at school generally, and this is supported by better emotions and motivation. As application time was a significant predictor for learning emotions, school start times probably need to be rethought.

Disclosure statement

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Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.