A Mentor-based intervention for the intrinsic motivation of secondary school children

Andrew Sutjahjo, Student Number: 5873185Date: 03-11-2014

A Mentor-based intervention for the intrinsic

motivation of secondary school children

ABSTRACT

This explorative mid-term, longitudinal study evaluated the effectiveness of an

autonomy-supportive, single-session mentor-based intervention on the intrinsic

motivation of higher general education students. The initial hypotheses were

based on the Excellence segmentation model, however, the Excellence model

turned out to be an extremely bad fit for the data. Thus post-hocs with statistical

corrections were performed with the subscales of the Excellence model. A

decrease in intrinsic motivation was found within the boys’ intervention group.

While no other significant differences were seen, trends were found pointing

towards the intervention modulating predictive relationships between subscales

of the Excellence model and changes in subscales of intrinsic motivation. The

intervention group showed an inverse relationship between extrinsic motivation

and change in competence, and the intervention group did not show the inverse

relationship between drive to perform and change in intrinsic motivation. Further

research could be performed to confirm if the intervention can be helpful to

enhance competence for students with low extrinsic motivation, or to protect the

intrinsic motivation of students with a high drive to perform.

INTRODUCTION

SOCIETAL BENEFITS: MOTIVATION DECREASE IN SECONDARY EDUCATION

In 2008 the Dutch government invested in projects to promote excellence within higher education. Part of the challenge in this promotion is how to get, and keep students motivated in scholastic work. It’s difficult to convince the youth to learn about math or French when there’s a plethora of other things to do, and they believe that they will never use this knowledge in their future.

Motivation of adolescents in the Netherlands has already been low (ref zie boek) when compared to the rest of Europe. This level of motivation drops even further when adolescents start their secondary education (Opdenakker, Maulana & Den Brok, 2012), which is attributed to the transition between primary and secondary education. Gottfried, Fleming and Gottfried (2001) suggest that intrinsic motivation among secondary school students further decreases over the years. This can have detrimental consequences for students’ performance and ultimately the tendency to stay in school (Alexander, Entwisle & Horsey, 1997). Thus it is important to examine methods to facilitate scholastic motivation within this particular at-risk group.

However, what can be done to promote motivation often remains a difficult question for teachers and researchers to answer. Reeve et al (2004) suggest that students can be motivated by creating an environment that supports their intrinsic need to learn. In this study the medium-term effects of an intervention, which integrates creating a supportive environment, is tested in terms of motivation of students in

pre-vocational secondary education.

THEORETICAL FRAMEWORK

Various theories on motivation focus on different aspects of motivation: from the interplay between expectancy and value of success (Atkinson, 1964), to focusing on goals (Boekaert, 2009) and intrinsic motivation (Ryan & Deci, 2000). In this study we will use the Self-determination theory (SDT; Ryan & Deci, 2000) as a framework for enhancing motivation.

SDT is based on the tenet that each person is by nature intrinsically motivated, curious and interested in learning. However, people can be inhibited in this drive to

learn through interactions within their social environments. They propose three psychological needs that must be fulfilled to allow this intrinsic motivation to thrive: a need for competence, a need for social relatedness, and a need for autonomy. Satisfaction of these needs contributes to a feeling of well-being in adolescents (Véronneau et al. 2005) and allows intrinsic motivation to flourish, which creates an optimal learning environment (Lüftenegger et al. 2012). Indeed, studies that are focused on improving intrinsic motivation showed increased depth of processing, test performance, and persistence in first year college students (Vansteenkiste et al. 2004). In general, higher intrinsic motivation correlates with higher cognitive engagement (Walker et al. 2006) and autonomy. Autonomous motivation has also been shown to reciprocally increase pleasant affect in young adolescents (Pomerantz 2013)

Increasing Intrinsic Motivation

Teachers and mentors can have a large effect on the fulfillment of these three needs by their choice in interaction styles with their students. According to Ryan & Deci (2000), these styles can range from being highly controlling of their students’ actions, to highly autonomy supportive. Autonomy supportive teachers generally identify, and nurture students’ needs and preferences and create opportunities for students to follow these internal motivations. On the other side of the spectrum, controlling teachers generally posit a teacher-generated curriculum which decides what the student will learn. Students of autonomy supportive teachers show more

engagement (Reeve et al. 2004) more depth of processing (Vansteenkiste et al. 2004), assess that they’ve learnt more (Lüftenegger et al. 2012), and tautologically, report a higher perceived autonomy (Reeve & Jang 2006).

A promising intervention to jumpstart a change in interaction styles is the

Appreciative Inquiry method (AI; Cooperrider & Whitney, 2001). The AI method is a dialogue form in which the experiences of the people in the dialogue are the focus of discussion. Participants are allowed to tell stories based on the subject in mind (their goals and motivations regarding their study), and are encouraged to speak from their own experiences and beliefs. Verleysen (2013) found that applying the AI method at least 4 single sessions a year increased the intrinsic motivation and self-reported autonomy in the workplace at the end of that year. AI also enhances personal

relationships between faculty and students, which prevents drop-out (Scheel, 2009) and facilitates social relatedness.

A different intervention approach by Daki & Savage (2010) found that five 40-minute interventions by a mentor could increase at-risk students’ competence in reading by asking them to picture, and focus on what it would feel like when the student finds the solution to their problem. Strangely enough, while their competence increased, their intrinsic motivation for reading decreased.

GAP BETWEEN RESEARCH AND PRACTICE

Prima facie it seems like the extent of our search for interventions is clear: Find out what works from a theoretical standpoint and design an intervention from there, train the teachers and analyze the data that returns to you. The teachers are trained in a scientifically backed intervention, and researchers obtain data to validate or reject their hypotheses: a win-win situation. In reality, however, there is a gap

between theory and practice which has stymied the implementation of interventions (De Corte, 2000; Prince, 2014; Brown, 1992).

In short, disciplinary, scientific research aims to change an independent variable, and control all other possible variables to see what the effect of the independent variable is on the dependent variables and what the reasons behind these effects are. To maintain a high amount of experimental precision, this usually is a relatively small variable, which begets a small change. Classrooms, teachers and school directors on the other hand, aim to learn and utilize interventions that benefit the classroom as much as possible; interventions need to be relevant to the classroom. High

experimental precision interventions are less interesting for teachers to invest their time in, as the benefits do not outweigh the costs. Teachers and school directors are already overworked, and have limited time. Thus some concessions need to be made on the experimenter side to accommodate spending the teachers’ and directors’ time.

In such, we designed a single session intervention which communally incorporates Appreciative Inquiry, autonomy supportive behavior and communal goal setting. EXCELLENCE MODEL

Adolescents differ in their ideas on excelling in education. In 2011, YoungWorks, together with Platform Bèta Techniek and Motivaction, constructed a model that segments adolescents into types of excellence with the goal to personalize methods

of stimulating adolescents to excel in education. 26 group interviews with 120 adolescents yielded statements on excellence in education. These statements were used to develop a self-report questionnaire with 7 dimensions of excellence. This questionnaire was filled in by 1337 adolescents (age 12-25) and on the basis of a factor analysis, four types were identified: Self-assured Generalists, Easygoing Fun-lovers, Compliant Followers, and Ambitious Status Seekers (Youngworks &

Motivaction 2011). Both Self-assured Generalists and Ambitious Status Seekers scored higher on intrinsic motivation, and drive to perform than the other types, suggesting that different types demand different approaches in order to increase their motivation to excel in education.

The aim of this study was to evaluate the effect of a mentor training and the subsequent intervention between mentor and student on the students’ scholastic motivation over a two month timespan. Furthermore this study will evaluate the difference in effectiveness of this intervention for students in different excellence model segments.

The expectations based on literature were that the perceived competence, intrinsic motivation and autonomy of students’ would decline over time. The intervention, however, was expected to have a positive effect on the students’ perceived

competence, intrinsic motivation, and autonomy.

Furthermore, given their lower drive to perform and intrinsic motivation, we expected that the Compliant Followers and Easygoing Fun-lovers would benefit the most from the extra attention and guidance of the intervention than the other two excellence types.

MATERIALS & METHODS

The participants were 164 Dutch students attending 1st_{, 2}nd _{or 3}rd _{grade of higher}

general secondary education (havo) in a school in the suburbs of Amsterdam. Of these 164 students, 52 fully completed both pre- and post-measurements. The distribution boy girl was 52% boy, 48% girl.

To test the longitudinal effect of the intervention, a pre-post experimental control group design was used. One treatment group was used, consisting of students of different grades, and one control group also consisting of different grades. The treatment group was asked to talk to their mentor, during which the intervention was performed. Both the treatment group and the control group received a list of tips from their mentor for preparing for their exams before the first intervention was performed. It was opted to combine the grades in order to compensate for the large amount of incompletes. The subjects were pseudo-randomly selected in order to match the distribution of excellence model types in the student population. The numbers of participants per condition per measurement are represented in Table 1. The intervention was implemented between the pretest and the post-test. The intervention was implemented using a two-hour training session.

The participants of the intervention training sessions were the (prospective) mentors of the students participating in the study. Trainers were the first author and a

colleague. The school administrators had communicated that participation of training sessions was mandatory. Of the 10 mentors, six attended the training.

Implementation fidelity was judged using evaluation forms filled out by mentors after each intervention. The student outcomes were measured at two time-points: a before-intervention pre-test 3 months before the end of the school year and a post-test in the final week of the school year. The students’ Excellence model type was measured during the before-intervention pre-test.

Table 1. Number of participants per measurement

Group Pre-test Post-test Matched*

Control 129 69 32

Intervention 35 21 20

Note. * Participants that fully completed both tests and filled in the same name on

each.

The mentors were subjected to a 2-hour training which emphasized the importance of listening authentically, gave tips for showing autonomy supportive behaviour, and how to guide students to set internalized goals. This time was also used to explain the experimental set-up and the students they would perform the intervention on. Mentors also received guidelines which stipulated how to start the intervention, and possible questions for the conversation, see appendix a.

Both treatment and control groups received a sheet of tips on how to prepare for their end of years exams (see appendix b). Only the treatment group further discussed these tips with their mentors.

Instruments:

All instruments were self-report questionnaires and were answered either

electronically or paper-pencil. The pre-test questionnaire was paper-pencil, the rest was electronic at the request of the school. For example items and scale reliabilities, see table 2.

Motivation – The Dutch translation of Ryan and Deci’s Intrinsic Motivation Inventory (IMI, Validated by Meijer & Van Eck, 2008) was used, which comprised of three subscales each using a 7-point likert scale: the Competence subscale consisted of 10 items concerning the students’ own perceived competence at school. The Intrinsic Motivation subscale consisted of seven items, concerning the extent the students’ perceived intrinsic motivation for school. The autonomy subscale consisted of seven items, concerning the perceived autonomy of the students at school.

Cronbach’s ⍺ for this subscale was slightly below the Cronbach and Shavelson (2004) standard of acceptable reliability ( ≥.70) during the pre-test condition, but was still used. Conclusions with regard to autonomy will be drawn with caution.

The Intrinsic motivation to perform subscale consisted of seven items concerning to what extent a student is looking for a challenge.

The Drive to perform subscale consisted of three items concerning to what extent the student feels the need to perform now in order to be successful in the future.

The Culture of mediocrity subscale consisted of four items concerning how unimportant is it for the student to achieve high grades.

The Awareness of the Future subscale consisted of three items concerning how clear a picture the student has of the future.

The Social deterrent subscale consisted of three items concerning to what extent the student’s friends disapprove of excellence.

The Extrinsic motivation to perform subscale consisted of six items concerning to what extent students want to excel in order to achieve a specific goal.

The Educational Challenge subscale consisted of three items concerning to what extent students are challenged at school.

Cronbach’s α for the Culture of mediocrity and Social deterrent subscales were below the Cronbach and Shavelson (2004) standard of acceptable reliability (0,257 and 0,491) respectivelyEach item was weighted before summed in the subscale and median split into high and low values. A Bayes and Monte Carlo cluster analysis was used by Youngworks & Motivaction (2011) in the development of the Excellence model using the statistical toolbox Latent Gold. When the parameters of this analysis were performed on the current dataset an extremely bad fit was found (L2_{= 260,75,}

df = 94, p= 1,3 x 10-17_{, R}2 _{= 0,49). Thus the Excellence model was discarded as a toolset}

for this study and the unweighted sum scores of its subscales that passed Cronbach’s standard of acceptable reliability were used.

Table 2. Example items and internal consistencies of self-report questionnaire scales used in the study

Example item No. of items Source Scale reliability pre-test Scale reliability post-test

Perceived Competence

"Het lukt mij om geen onvoldoendes op mijn

rapport te halen" 10 Meijer & Van Eck (2008) 0,907

Autonomy

"Ik vind dat ik zelf kan beslissen hoe ik opdrachten

voor school uitvoer" 7 Meijer & Van Eck (2008) 0,676

Perceived Intrinsic Motivation

"Ik zou de lessen op school omschrijven als

interessant" 7 Meijer & Van Eck (2008) 0,811

Intrinsic motivation to perform

"Als ik iets leuk vind, wil ik

daar in uitblinken" 7 Youngworks (2011) 0,751

succesvol te zijn, moet ik nu alvast hard werken" Culture of

mediocrity

"Cijfers vind ik niet belangrijk, zolang ik mijn

diploma maar haal" 4 Youngworks (2011) 0,25

Awareness of the future

"Ik heb een duidelijk beeld van wat ik over vijf jaar

bereikt wil hebben" 3 Youngworks (2011) 0,827

Social deterrent

"Als ik heel hard mijn best doe op school vinden

anderen dat gek" 3 Youngworks (2011) 0,424

Extrinsic motivation to perform

"Bij een wedstrijd wil ik

altijd winnen" 6 Youngworks (2011) 0,722

Educational

"De vakken op de middelbare school zijn

Analysis:

Multiple regression analyses were used to analyze the relationship between the Excellence subscales and changes in motivation between the pre- and post-test. Further effects on this relationship due to the intervention were tested using the Student’s t-test to test the difference in regression slopes between the two treatments.

As the model on which the hypotheses were based was discarded, and new hypotheses were tested, Bonferroni corrections were performed on all statistical tests. Five subscales of the excellence model were used, thus we used an alpha level of .01 (.05/5) for the regression analyses.

Furthermore, educational interventions are known to have small to medium effect sizes (0.40, Hattie, 2012 (as cited in Prince 2014)), next to an alpha of .01, an alpha of .02 was also used. Tests were performed two sided, as no clearly defined

hypotheses could be formulated and analyses of the effects are explorative.

RESULTS

The teachers indicated during their evaluations of each intervention that they had followed the guidelines and tried to connect to, and listen to each of the students they had an intervention with.

They scored the intervention at a 6.80 (SD=0.80), and gave feedback such as: “[the guidelines]Give structure, it reminds the mentor to ask open questions” “This talk/questionnaire is very helpful to help motivate students, but it would be better to use it at the start of the school year (after around 2-4 weeks)”

Differences between gender

In the pre-test, a significantly higher competence was found in boys compared to girls t(116)=1,991, p=0.049, a nearly significant higher IM t(116)=1.882, p=0.062, and higher Autonomy t(116)=1.728, p=0.087 was found in boys compared to girls. The post-test measurement showed no difference between the two genders for competence t(50)=-0.446, p=0.658, IM t(50)=0.268, p=0.79, nor Autonomy t(50)=0.318, p=0.752.

Differences between years

In the pre-test, a significantly lower Competence was found in first grade students compared to both second grade students t(61)=-2.093, p=0.04 and third grade students t(83)=-2.478, p=0.02.

Pre- and Post-test comparisons

No significant differences were found between the pre- and post-test measurements with respects to competence, Intrinsic motivation, nor Autonomy subscales in either experimental or control conditions. A decrease in IM was found in the experimental condition that approached significance t(19)=-0.849, p=0.08.

This can be attributed to the boys, as boys in the experimental condition were found to have a significant decrease in IM t(8)=-2.953, p=0.02. No gender difference was found, however, between the change in IM within the experimental condition t(18)=-1.277, p=0.218.

Table 3. Means of pre-and post-intervention motivation subscales

Intervention Control Mean Std.

Deviation Mean Std. Deviation Competence pre-test 4,23 1,37 4,80 1,13 Competence post-test 4,26 1,24 4,66 1,22 Perceived Intrinsic motivation pre-test 3,86 1,17 3,81 1,12 Perceived Intrinsic motivation post-test 3,62 1,06 3,67 1,04 Autonomy pre-test 4,13 0,92 4,34 0,88 Autonomy post-test 3,94 0,98 4,28 1,00

A significant decrease in competence was found in boys t(24)=-2.729, p=0.01. There was no significant difference between experimental and control conditions

While the increase of competence of girls was not significant t(26)=1.593, p=0.123, there was a significant gender difference in the change of competence over time t(50)=2.891, p=0.006.

Regression intervention and Excellence subscales

Post Hoc regression analyses were conducted using Bonferroni adjusted alpha levels of 0,01 per test (0.05/5), and additionally with an alpha of 0.02 due to small effect sizes within educational interventions.

This indicated that the Drive to perform subscale predicted the change in IM (b = -0,617, t=-3.329, p=0,002). Drive to perform also explained a significant proportion of variance in change in intrinsic motivation R2=0.165 F(1,50)=11.08, p=0.002. Within the experimental condition this was not found (b= -0.315, t=-1.149, p = 0.266), while within the control condition a significant prediction was found (b=-0.680, t=-2.655, p = 0.013). Considering a trend based on gender was found for change in intrinsic motivation, a multiple regression analysis was conducted to see if gender and/or drive to perform predicted a change in intrinsic motivation due to the intervention. The data conformed to the assumptions of collinearity (gender, Tolerance = 0.996, Drive, Tolerance=0.996, VIF =1.004), independent errors (Durbin-Watson=2.2), normality, homogeneity of variance and linearity.

When controlled for gender, the intervention did not significantly change the predictive power of drive to perform on the change in intrinsic motivation (t(48)=1.11, p=0.27).

The means of the drive to perform subscale was nearly significantly higher in the experimental (M=2.47, SD=0.47) than in the control (M=2.18, SD=0.57) condition; t(50)=-1.922, p=0.06, thus conclusions concerning drive to perform and the intervention will be made with caution.

The Extrinsic motivation subscale nearly significantly predicted the change in

competence within the experimental condition (b=-1.287, t=-2.415, p = 0.027), while within the control condition this prediction was not found (b=-0.229, t=-0.580, p = 0.566). However, when compared, there was no significant change in predictive power of extrinsic motivation on change in competence due to the intervention (t(48)=1.62, p=0.11).

DISCUSSION

The aim of the study was to explore the effects of an intervention based on

autonomy-supportive behavior, authentic listening, and setting goals together by a student’s mentor in terms of the motivation of the student.

While previous studies had shown a decline in students’ motivation from the start of secondary education, our baseline measurements showed that first grade students had a lower motivation as compared to second and third year students. The means of all IM subscales were lower for the first year students, however only the competence subscale reached significance. This supports Opdenakker’s (2012) hypothesis that motivation drops in the transition between primary and secondary education.

Before further discussing the results it is prudent to note that an abysmal percentage of participants fully completed both pre- and post-tests (32%). This leaves selection bias as a very real concern, and further discussion on longitudinal results will be done with caution.

While the intervention did not produce significant effects in any of the motivation subscales of students in general, we found that there was a gender difference. Boys’ competence and intrinsic motivation seemed to be negatively affected in the

intervention condition. While the decline in competence was also found in the control group, and can be attributed to factors beyond the intervention, the

decreased intrinsic motivation was not found in the control group. This can possibly be attributed to the decline in competence in boys. Daki and Savage (2010) found that most of their sample had a competence based strategy for enjoying reading. Given that the competence of boys decreased, it is conceivable that this decrease in perceived competence in academics also led to a decrease in enjoyment in reading. An interaction of this mechanism with the added attention to academics could have led to the decrease in intrinsic motivation of boys in the intervention group.

There is evidence, however, that this could be a selection bias anomaly. Previous research shows that while there are small gender differences in motivation, these seem to be differences in degree, and not fundamental differences (Martin, 2004).

Furthermore, a meta-analysis by Roorda et al. (2011) found that there are no gender differences in how student-teacher interactions affect students’ academic motivation. The intervention affected the relationship between subscales of the Excellence model and changes in the motivation in a student. In particular, an inverse relationship was found between the extrinsic motivation subscale and the change in the student’s perceived competence within the intervention group. This subscale measures the extent to which students want to excel in order to achieve a specific goal not set by themselves. The intervention helped students to autonomously set goals for

studying, and the increased perceived competence for students which scored low on the extrinsic motivation subscale did not come as a surprise. The decrease in

perceived competence in the students with high extrinsic motivation after the intervention, however, was unexpected. At present moment there are no hypotheses on the mechanism of this relationship.

In contrast, students’ drive to perform: to what extent they identify with needing to perform now in order to be successful in the future, already shows predictive power for the change in intrinsic motivation of students over time. A higher drive to

perform predicts a negative change in intrinsic motivation between the month leading up to- and right after the students’ final exam of the year; conversely, a lower drive to perform predicts a positive change in intrinsic motivation. The students in the intervention group did not show this relationship, although there was no

significant difference between the regressions of the control and intervention group. A conceivable mechanism for this possible negating effect could be that those with a high drive to perform are already busy with preparing for their exams. The

intervention could help these students set intermittent goals for themselves,

increasing their intrinsic motivation. The students with a low drive to perform, on the other hand, might not be busy with their end of year exams, and the intervention itself could draw attention to that set deadline. This deadline could in turn decrease their intrinsic motivation (Amabile et al. 1976)

Limitations and further research:

Some limitations can be noted in the experimental design. Only one school participated in the experiment, which raises the chance that the found effects are

due to school variables as opposed to the intervention. Another limitation lies in the method of randomization process, which was pseudorandom, controlled for

Excellence model segmentation types. As this model turned out to fit our data very poorly and some of its subscales were unreliable, we discarded the model.

Unfortunately its insufficiencies were only found after the segmentation was made. This randomization and the low completion rate by students resulted in unequal groups when looked at in certain variables such as the drive to perform subscale. The low completion rate limited the study further. While there were enough participants per treatment for basic statistical tests, the small effect sizes required larger samples than we had.

The insufficiency of the Excellence model also led to discarding the initial hypotheses and thus required the use of statistical corrections to test post-hoc hypotheses, which lowered the already low power. Both of these complications meant that no further examinations of possible interaction effects could be performed.

Some controls were performed such as providing all the materials used in the intervention to both the intervention and the control group. It should be noted, however, that no sufficient control was implemented for the attention given by the mentor to the students in the intervention group. Thus a Hawthorne effect is not ruled out.

While the statistical tests show significant decreases in boys’ intrinsic motivation in the intervention condition, other trends can also be seen. The intervention could affect the relationships between extrinsic motivation and the change in competency, and between drive to perform and change in intrinsic motivation. This suggests that the intervention could be useful for female students with low levels of extrinsic motivation, as well as students with a high drive to perform.

References

Amabile, T. M., DeJong, W., & Lepper, M. R. (1976). Effects of externally imposed deadlines on subsequent intrinsic motivation. Journal of personality and social psychology, 34(1), 92. Brown, A. L. (1992). Design experiments: Theoretical and methodological challenges in creating complex interventions in classroom settings. The journal of the learning sciences, 2(2), 141-178. Cooperrider, D.L. & Whitney, D. (2001) A positive revolution in change: Appreciative Inquiry. In D. Cooperrider, P. Sorensen, D. Whitney, & T. Yaeger (Eds.), appreciative inquiry (pp, 4-24_. Champaign, IL: Stipes.

Cronbach, L. J., & Shavelson, R. J. (2004). My current thoughts on coefficient alpha and successor procedures. Educational and Psychological Measurement, 64, 391-418.

Daki, J., & Savage, R. S. (2010). Solution-focused brief therapy: Impacts on academic and emotional difficulties. The Journal of Educational Research, 103(5), 309-326.

De Corte, E. (2000). Marrying theory building and the improvement of school practice: A permanent challenge for instructional psychology. Learning and instruction, 10(3), 249-266.

Deci, E. L., Koestner, R. & Ryan, R.M. (1999) A meta-analytic review of experiments examining the effects of extrinsic rewards on intrinsic motivation. Psychological Bulletin, 125(6), 627-668

Deci, E. L., & Ryan, R. M. (2008). Facilitating optimal motivation and psychological well-being across life's domains. Canadian Psychology/Psychologie canadienne, 49(1), 14.

Gottfried, A.E., Fleming, J.S., & Gottfried, A.W. (2001). Continuity of academic intrinsic motivation from childhood through late adolescence: A longitudinal study. Journal of Educational Psychology, 93, 3–13. Lee, H., & Kim, Y. (2014). Korean adolescents’ longitudinal change of intrinsic motivation in learning English and mathematics during secondary school years: Focusing on gender difference and school characteristics. Learning and Individual Differences.

Lüftenegger, M., Schober, B., van de Schoot, R., Wagner, P., Finsterwald, M., & Spiel, C. (2012). Lifelong learning as a goal–Do autonomy and self-regulation in school result in well prepared pupils?. Learning

and Instruction, 22(1), 27-36.

Martin, A. J. (2004). School motivation of boys and girls: Differences of degree, differences of kind, or both?. Australian Journal of Psychology, 56(3), 133-146.

Meijer, J., & van Eck, E. (2008). Leren met meer effect; rapportage van het onderzoek.

SCO-Kohnstamm Instituut van de Universiteit van Amsterdam.

Moller, A. C., Deci, E. L., & Elliot, A. J. (2010). Person-level relatedness and the incremental value of relating. Personality and Social Psychology Bulletin, 36, 754–767.

Prince, A. (2014). Motivation: How to tame the elephant in the classroom?. (unpublished doctoral

dissertation). Rijksuniversiteit Groningen, Groningen.

Reeve, J., Jang, H., Carrell, D., Jeon, S., & Barch, J. (2004). Enhancing students' engagement by increasing teachers' autonomy support. Motivation and emotion, 28(2), 147-169.

Reeve, J., & Jang, H. (2006). What teachers say and do to support students' autonomy during a learning activity. Journal of Educational Psychology, 98(1), 209.

Roorda, D. L., Koomen, H. M., Spilt, J. L., & Oort, F. J. (2011). The influence of affective teacher–student relationships on students’ school engagement and achievement a meta-analytic approach. Review of

Educational Research, 81(4), 493-529.

Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American psychologist, 55(1), 68.

Scheel, M. J., Madabhushi, S., & Backhaus, A. (2009). The academic motivation of at-risk students in a counseling prevention program. The Counseling Psychologist.

Van Nuland, H. J., Taris, T. W., Boekaerts, M., & Martens, R. L. (2012). Testing the hierarchical SDT model: the case of performance-oriented classrooms. European journal of psychology of

education, 27(4), 467-482.

Vansteenkiste, M., Simons, J., Lens, W., Sheldon, K. M., & Deci, E. L. (2004). Motivating learning, performance, and persistence: the synergistic effects of intrinsic goal contents and autonomy-supportive contexts. Journal of personality and social psychology, 87(2), 246.

Verleysen, B., (2013). The Relationship between Appreciative Inquiry and Employee Work Engagement Explained through Basic Psychological Needs. Retrieved from

http://www.academia.edu/4447081/De_relatie_tussen_Appreciative_Inquiry_en_Bevlogenheid_VERL EYSEN2013

Véronneau, M. H., Koestner, R. F., & Abela, J. R. (2005). Intrinsic need satisfaction and well–being in children and adolescents: An application of the self–determination theory. Journal of Social and

Walker, C. O., Greene, B. A., & Mansell, R. A. (2006). Identification with academics, intrinsic/extrinsic motivation, and self-efficacy as predictors of cognitive engagement. Learning and Individual

Differences, 16(1), 1-12.

Youngworks & Motivaction, (2011). The Excellence Model: Young people on excellence. Retrieved from http://www.excellentiemodel.nl//docs/Documentatie/120509_ym_excelleren_eng_lores.