22. Research in Higher Education
Paper
Higher-Order Thinking Skills in Academic Courses Based on SEL Principles
Sigal Chen
Levinsky-Wingate academic college, Israel
Presenting Author: Chen, Sigal
The research deals with how higher-order thinking of education students, expressed in reflective writing and metacognition, promotes professional insights as part of a social-emotional learning process. The findings reveal significant personal and professional insights among the students, which may serve as a bridge to intellectual development that manifests in social, emotional, and cognitive development.
social-emotional learning (SEL)
General skills of social-emotional learning are reflected in this study in three central dimensions of generic skills and abilities: (1) cognitive self-regulation, for example, control of attention, work planning, and cognitive flexibility (2) emotional processes, for example; Emotional awareness, emotional expression, behavior regulation, empathy (3) Cultivating social skills while understanding social cues, conflict resolution, pro-social behavior (Jones and Bouffard, 2012).
Social-emotional learning is reflected in learning emotional and social skills and concepts used by the students in contexts and social situations during the courses. This is reflected in the emotional aspect, including, for example, empathy, teamwork, informed decision-making, regulation, perseverance, and dealing with failure. Studies (Jones et al., 2019; Blyth et al., 2018; Jones & Kahn, 2017) that examined social and emotional development demonstrated that they are intertwined and affect academic achievement, physical and mental health, and civic engagement. Moreover, it has been proven that cultivating social and emotional abilities in learners can predict an improvement in academic achievements, reduce situations, deal with crises, accelerate social leadership, and influence learning applications and their mental and emotional fitness. Thus, social-emotional learning has received much attention in the last decade and includes many concepts and organizing theories of these skills and competencies (Sperling, 2018).
higher order thinking
High thinking allows learners to be aware of their learning process and control their decisions while paying attention to the entire learning process. This process requires different decisions during learning and provides tools to deal with difficulties while thinking about successful and varied solutions whose contribution to the quality of education is significant (Ben-David and Orion, 2013; Zohar and Barzilai, 2015; Perry, Landy, and Golder, 2019).
The research literature uses the terms reflection, metacognition, and self-directed learning interchangeably, although there are theoretical differences between them (Veenman, 2011). Metacognition researchers tend to believe that self-directedness is a corresponding component of metacognition. In contrast, researchers refer to self-direction as a concept containing metacognition alongside concepts such as motivation and emotional regulation (Veenman et al., 2006). Many studies have indicated that learners who did higher thinking processes, such as reflective and metacognitive processes, discussed more self-examination activities and demonstrated a deeper understanding of the study material compared to groups that did not learn strategies advocating this type. of thinking (Kaberman & Dori, 2009; Zohar, & Barzilai, 2015).
The reflective and metacognitive process may influence and shape the hidden pedagogical beliefs and concepts directly affecting teaching. To achieve this, it is essential to include in the practical experience during training elements of building complex educational processes: beliefs and attitudes about teaching and its components (Osterman & Kottkamp, 2004; Korthagen, 2004). Zohar and Barzilai (2015) point out that the skills required to implement this type of thinking are (1) planning - setting goals, choosing an appropriate strategy, (2) monitoring - awareness and examination of the thinking processes during learning, and (3) evaluating the thinking. And learning processes, which are carried out at the end of the work process through reflection and self-evaluation, may lead to operative recommendations regarding learning.
Methodology, Methods, Research Instruments or Sources UsedThe research population includes 48 students aged 25-34, socially diverse and studying at a large college in Israel. The students participated in two courses for a bachelor's degree in education: some in the course 'Language assessment processes in the upper elementary school' and others in the class 'Discourse investigation.'
research process
The study is based on data collected after the researchers published the course grades and after the participants agreed to use the reflective processes they wrote during the courses for research purposes. One of the researchers taught a seminar on discourse research. The second researcher taught the Department of Hebrew a disciplinary course on language skills assessment. In both courses, the students were asked, as part of the course assignments, to do a reflective and meta-cognitive process in which they shared their thinking, insights, and professional self-formation.
Each researcher collected and analyzed the records while identifying key themes and finding connections between them. Processing was based on content analysis focusing on what the students said in words, and descriptions, rather than how they presented their words (Braun & Clarke, 2006). In the analysis phase, each researcher reads all the interview transcripts to determine which category the segment belongs to according to the research objectives. In the second step, the matching of the elements to the categories was selected. The reliability test was based on "reliability between judges." In individual cases where differences of opinion were discovered, a discussion was held until an agreement was reached. The reliability between the judges is 84%.
The study is a qualitative study based on the researcher's ability to internalize the complexity of the learned learning experience and the context. A researcher is an interpretive tool for reality. His interpretations are derived from the various contexts in which the research participants operate and reveal the meanings, interpretations, and subtleties given to the fact. Reality is influenced by personal and personality and social, verbal, and cultural structures (Guba & Lincoln, 2008). The role of the researcher is to investigate the phenomena, find meaning and interpret the phenomena, thus allowing to learn in-depth about the process being studied (Denzin & Lincoln, 2000). The interpretive-qualitative aspect helps researchers explore the students' experiences being studied.
Conclusions, Expected Outcomes or FindingsReflection and metacognition are considered high-thinking skills. They allowed the students in the courses to stop, think and conduct an intra-personal dialogue about topics that came up, choose between alternatives, and decide which system of beliefs, perceptions, and ideas suits them.
The research findings revealed personal and professional insights.
The personal insights were essential for increasing the components of social-emotional learning: emotional awareness, self-management, and the students' collaborative learning skills.
The writing, the description of the events, and the need to conceptualize the feelings and perceptions encouraged the students to rethink the experiences, interpret them and examine their consequences.
The students examined the feelings, perceptions, actions, and decisions and drew lessons they can apply as teachers in diverse teaching contexts in the educational field.
Examining the professional insights shows that the students cultivated abilities to respond to differences in the classroom, self-manage, integrate principles of social-personal learning, and interact with colleagues in the teachers' room. The higher-order thinking emphasized the importance of providing a differential response in the classroom and adapting the learning framework, teaching methods, and assessment to the diverse students - these increase motivation, ignite curiosity and interest, sharpen academic skills, and encourage choice and exploration.
A high order of thinking deepened the understanding of the study material, which includes three central dimensions of social-emotional skills and abilities: cognitive self-regulation, emotional awareness, regulating behavior, and evaluating the diversity in the group.
The introspection strengthened the concept that the students as teachers in the future have the responsibility to also incorporate principles of social-emotional learning in their classrooms, to serve as a source of support, to be present, to reassure, to allow autonomy, to maintain a routine and to provide a sense of partnership.
ReferencesBen-David, A., & Orion, N. (2013). Teachers’ Voices on Integrating Metacognition. Science Education. International Journal of Science Education, 35(18), 3161–3193. https://doi.org/10.1080/09500693.2012.697208
Blyth, D. A., Jones, S., & Borowski, T. (2018). SEL frameworks–What are they, and why are they important? Measuring SEL, Using Data to Inspire Practice, 1(2), 1-9.
Bransford, J., Darling-Hammond, L. & LaPage, P. (2005). Introduction. In: L. Darling-Hammond & J. Bransford (Eds.), Preparing teachers for a changing world: What teachers should learn and be able to do (pp. 358- 389). Jossey-Bass.
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative research in psychology, 3(2), 77-101.
Denzin, N. K., & Lincoln, Y. S. (2008). Introduction: The discipline and practice of qualitative research. In N. K. Denzin & Y. S. Lincoln (Eds.), Strategies of qualitative inquiry (pp. 1–43). Sage Publications, Inc.
Guba, E. G. & Lincoln, Y. S. (1998). Competing paradigms in qualitative research. In N. K. Denzin & Y. S. Lincoln (eds.), The Landscape of Qualitative Research (pp. 195-220). Publications.
Kaberman, Z., & Dori, Y. J. (2009). Metacognition in chemical education: Question posing in the case-based computerized learning environment. Instructional Science, 37, 403–436. https://doi.org/10.1007/s11251-008-9054-9
Korthagen, F. A. J. (2004). In search of the essence of a good teacher: Towards a more holistic approach in teacher education. Teaching and Teacher Education, 20(1), 77-97.
Jones, S., Farrington, C.A., Jagers, R., Brackett, M., & Kahn, J. (2019). Social, emotional, and academic development: A research agenda for the next generation. National Commission on Social, Emotional, and Academic Development.
Osterman, K. F., & Kottkamp, R. B. (2004). Reflective practice for educators. Thousand Oaks, CA: Corwin Press.
Perry, J., Lundie, D., & Golder, G. (2019). Metacognition in schools: what does the literature suggest about the effectiveness of teaching metacognition in schools? Educational Review, 71(4), 483–500.
https://doi.org/10.1080/00131911.2018.1441127
Veenman, M. V. J. (2011). Learning to self-monitor and self-regulate. In R. E. Mayer & P. A. Alexander (Ed.), Handbook of research on learning and instruction (pp. 197–218). Routledge.
Veenman, M. V. J., Van Hout-Wolters, B. H. A. M., & Afflerbach, P. (2006). Metacognition and learning: conceptual and methodological considerations. Metacognition and Learning, 1(1), 3–14. https://doi.org/10.1007/s11409-006-6893-0
Zohar, A., & Barzilai, S. (2015). Metacognition and teaching higher-order thinking (HOT) in science education: Students’ learning, teachers’ knowledge, and instructional practices. In R. Wegerif, Li, Li & J. C. Kaufman (Eds.), Routledge International handbook of research on teaching thinking (pp. 229–242). Routledge.
22. Research in Higher Education
Paper
Latent Profiles of Undergraduate Students regarding Academic Procrastination and Achievement
Munevver Ilgun Dibek
TED University, Turkiye
Presenting Author: Ilgun Dibek, Munevver
In many computer-based learning environments (CBLEs), learners of all ages struggle to use critical cognitive and metacognitive self-regulation abilities (Azevedo, 2015). In this situation, choosing what to learn, when to learn it, and how long to study it becomes more crucial (You, 2015). Time usage, goal-setting, self-monitoring, self-reactions, self-efficacy, and motivation are the six self-regulatory processes that underlie all other activities (Zimmerman & Risemberg, 1997). Time management, motivation, and perceived self-efficacy play the most significant roles among these processes (Zimmerman, 1998). According to several studies (Kirk et al., 2013; Visser et al., 2015), it appears that time management plays a significant role in educational outcomes from K–12 to higher education. Given the intricacy of the self regulation construct, the researcher of this study concentrated on one of its dimensions in this paper: time management.
Numerous studies discuss the value of time management and learning, emphasizing both the quantity and quality of the learning time students spend on learning (Balkis, 2011). Several of these studies concentrate on academic procrastination, which is defined as the propensity to put off or even avoid performing an activity that is within one's control (Gafni & Geri, 2010). Procrastination, a minor but significant aspect of self-regulation, is especially examined in this study in an effort to understand its connection to student’s success in CBLEs. Specifically, this study made use of the existing model of Strunk (2012) for the study of procrastination and timely engagement because of the significance of the time that students spend learning and engaging in academic procrastination. According to model proposed by Strunk (2012), procrastination is on one side, with two distinct motivating inclinations. Procrastination for the sake of strategic advantage and an increase in work quality is defined as the procrastination approach. The avoidant coping type of procrastination is procrastination-avoidance. On the other hand, a time engagement approach is characterized by getting started on tasks right away to produce higher grades. To avoid the anxiety of failure that arises from delaying starting tasks, one would interact with them as soon as possible. This strategy is known as timely engagement-avoidance.
Although some research findings emphasize the negative effects of procrastination, others have identified a profile of active procrastination that corresponds to students who choose to delay work in order to achieve a superior performance (Choi & Moran, 2009; Kim & Seo, 2013). This contradiction makes it even more important to contextualize the research of this particular phenomenon in CBLEs.
CBLEs are prepared to gather significant amounts of data through user-machine interaction. Particularly, LMSs gather student data that, when examined properly, can give educators and researchers the knowledge they need to assist and continuously enhance the learning process (Paule-Ruiz et al., 2015). Modular Object Oriented Developmental Learning Environment (Moodle), a free LMS that enables the design of potent, adaptable, and interesting online courses and experiences, is one of the most popular (Rice, 2006).
This study aims to examine relationships among students course achievement and several time management-related features. In this regard, the following research questions are asked to answer:
1) How are the undergraduate students grouped based on the variables such as students course grade, time management-related features (the time differences between first access of students to course assignments and release dates of assignments, first access and submission dates, and submission dates and due dates)?
2) Are there statistically significant differences among different latent profiles regarding course grade and time management-related features?
Answers to these questions offer insight to scheduling and planning of the assessment methods in online courses.
Methodology, Methods, Research Instruments or Sources UsedParticipants
This study included data from two sections of an undergraduate course designed for pre-service teachers. Both sections (n1 = 44 and n2 = 44) were taught by the same instructor during the Fall 2021- 2022 semester. Due to COVID-19 pandemic, all course sections were delivered online. After the students completed the course, the log data and student grades from the LMS for each section were extracted.
Extraction of the Variables
Students' final course grade was determined based on the weighted average of three assignments. In this study, time-related features associated with students' course performance were extracted from the LMS log data. Specifically, the release date (i.e., when the assignment was made available to students), submission date, and due date of each assignment were used. Thus, the following time-related features from the LMS log data for each assignment were extracted: the time difference between first access and release dates, the time difference between first access and submission dates, and the time difference between submission dates and due dates. This feature extraction process yielded nine time-related features. Analysis were performed after the features were combined by calculating the means of features.
Data Analysis
After removing the missing and extreme cases, 58 students were included in this study. In terms of assumptions, the homogeneity of the variance assumption was violated. Regarding the first research question of this study, latent profile analysis (LPA) was conducted. LPA is a statistical procedure in which continuous latent indicators are utilized while performing latent class analysis (Muthén & Muthén, 1998-2017). Accordingly, Akaike Information Criterion (AIC), the Bootstrap Likelihood Ratio Test (BLRT), Bayesian information criterion (BIC), and the entropy value were used. Smaller values of AIC and BIC indicate a better model fit. Also, an insignificant (p > 0.05) BLRT indicates that adding more profiles into the model does not improve the model. Additionally, a value closer to 1.0 for the entropy values indicates a better decision on the number of profiles to include (Wang & Wang, 2020). Regarding second research question of this study, due to the violation of homogeneity of variance assumption, Kruskall Wallis Test which is a non-parametric version of the one-way ANOVA was performed to compare the profiles regarding the variables addressed in this study. LPA was conducted with “tidyLPA” package (Rosenberg et al., 2018) in R, and SPSS software was used for Kruskall Wallis Test.
Conclusions, Expected Outcomes or FindingsAfter LPA performed, model fit statistics were obtained to determine the optimal number of classes. According to model-fit statistics, the model that best fit the data was found to be the four-class model. General patterns of the profiles were plotted. Accordingly, those who are classified in Profile 1 have “timely-engagement avoidance” according to the model proposed by Strunk (2012). Additionally, those students classified in Profile 2 are called as students who “have timely engagement approach”. It was also found that Profile 3 included the students who had procrastination avoidance. Lastly, students classified in Profile 4 were found to have procrastination approach.
The Kruskall Wallis test results, which were conducted to determine whether the students gathered under these profiles differ in terms of course grade and time-related features were showed that the students in the four profiles differ significantly regarding course grade and time-related features. When the results of the Post-Hoc comparison made to determine which profiles caused this difference were examined, it was found that Profile 1 differed from Profile 2, Profile 2 differed from Profile 3 and lastly Profile 3 differed from Profile 4 regarding course grade. Additionally, regarding the interval between the date an assignment is released and the moment students have access to it, Profile 1 was different from Profiles 2, 3, and 4. Profiles 2, 3, and 4 were also different from Profile 1. Moreover, in terms of time difference between submission dates and due dates of the assignments, Profile 1 differed from Profile 4, Profile 2 differed from Profile 4, and Profile 3 differed from Profile 4. Also, regarding the time interval between first access to assignments and assignment submission deadlines, Profiles 1 and 2 and 3 differed from Profile 4. Lastly, it was found that Profile 2 and Profile 3 differed from Profile 4.
ReferencesAzevedo, R. (2015). Defining and measuring engagement and learning in science: Conceptual, Theoretical, methodological, and analytical issues. Educational Psychologist, 50, 84–94. https://doi.org/10.1080/00461520.2015.1004069
Balkıs, M. (2011). Academic efficacy as a mediator and moderator variable in the relationship between academic procrastination and academic achievement. Eurasian Journal of Educational Research, 45, 1–16.
Choi, J. N., & Moran, S. V. (2009). Why not procrastinate? Development and validation of a new active procrastination scale. The Journal of Social Psychology, 149(2), 195–212. https://doi.org/10.3200/SOCP.149.2.195-212
Gafni, R., & Geri, N. (2010). Time management: Procrastination tendency in individual and collaborative tasks. Interdisciplinary Journal of Information, Knowledge, and Management, 5, 115–125. https://doi.org/10.28945/1127
Kim, E., & Seo, E. H. (2013). The relationship of flow and self-regulated learning to active procrastination. Social Behavior and Personality An International Journal, 41(7), 1099–1113. https://doi.org/10.2224/sbp. 2013.41.7.1099
Kirk, D., Oettingen, G., & Gollwitzer, P. M. (2013). Promoting integrative bargaining: mental contrasting with implementation intentions. International Journal of Conflict Management, 24(2), 148–165. https://doi.org/10.1108/10444061311316771
Muthén, L. K., & Muthén, B. O. (1998-2017). Mplus user’s guide. (8th ed.). Muthén & Muthén.
Paule-Ruiz, M. P., Riestra-Gonzalez, M., Sánchez-Santillan, M., & Pérez-Pérez, J. R. (2015). The Procrastination related indicators in e-learning platforms. Journal of Universal Computer Science, 21(1), 7–22.
Rice, W. H. (2006). Moodle E-Learning Course Development: A Complete Guide to Successful Learning Using Moodle. Packt Publishing.
Rosenberg, J. M., Beymer, P. N., Anderson, D. J., Van Lissa, C. J., & Schmidt, J. A. (2018). tidyLPA: An R Package to Easily Carry Out Latent Profile Analysis (LPA) Using Open-Source or Commercial Software. Journal of Open Source Software, 3(30), 978, https://doi.org/10.21105/joss.00978
Strunk, K. K. (2012). Investigating a new model of time-related academic behavior: Procrastination and timely engagement by motivational orientation (Unpublished Doctoral dissertation). Retrieved from ProQuest Dissertation Publishing. (3554954)
Visser, L. B., Korthagen, F. A. J., & Schoonenboom, J. (2015). Influences on and consequences of academic procrastination of first-year student teachers. Pedagogische Studiën, 92, 394–412.
You, J. W. (2015). Examining the effect of academic procrastination on achievement using LMS data in e-learning. Educational Technology & Society, 18(3), 64–74.
Wang, J., & Wang, X. (2020). Structural equation modeling: Applications using Mplus. (2nd ed.). John Wiley & Sons.
Zimmerman, B., & Risemberg, R. (1997). Self-regulatory dimensions of academic learning and motivation. In G.D. Phye (Eds.), Handbook of academic learning: Construction of knowledge (pp. 105-125). Academic Press.
22. Research in Higher Education
Paper
Self-determined Motivation and Academic Buoyancy as Predictors of Achievement in Normative Settings
Görkem Aydın1, Aikaterini Michou2, Thanasis Mouratidis3
1Bilkent University, Turkiye; 2University of Ioannina, Greece; 3National and Kapodistrian University of Athens, Greece
Presenting Author: Aydın, Görkem
Academic buoyancy (Martin & Marsh, 2006) is students’ competence to respond effectively to academic daily setbacks and is considered an optimal characteristic of students’ functioning related to achievement. From the Self-determination theory perspective (SDT; Ryan & Deci, 2017), satisfaction of the need for autonomy, competence and relatedness and autonomous forms of motivation relate to students’ optimal functioning in schooling. Academic buoyancy, need satisfaction and autonomous motivation are important motivational constructs in the normative context of English preparatory programs (EPP) where students are required to achieve standard language skills to study in an English medium department. In the normative context of EPP, the ability to respond effectively to academic daily setbacks (i.e., academic buoyancy) is considered as an optimal characteristic of students functioning related to achievement (Martin & Marsh, 2008). To what extent, however, academic buoyancy can be predicted by students’ sense of need satisfaction (or frustration) and their autonomous versus controlled motivation in a normative context? If, despite the normative conditions of EPPs, need satisfaction and autonomous motivation could predict changes in students’ academic buoyancy and through it, success in EPP, then a need supportive motivational style could be suggested to EPP teachers to enhance students’ success.
In the present study, we investigated 1) whether students’ end-of-course (T2) academic buoyancy in the normative environment of EPP is predicted by their beginning-of-course (T1) self-determined motivation (operationalized as the degree of both students’ satisfaction of their psychological needs as well as autonomous versus controlled forms of motivation) while controlling for T1 academic buoyancy and 2) whether students’ T2 academic buoyancy mediates the relation between students’ T1 self-determined motivation and final (T3) academic achievement. In T1 and T2, 267 Turkish EPP students (females 56.9%; Mage = 19.11 , SD = 1.28) participated in the study. SEM analysis showed that T1 autonomous motivation and T1 controlled motivation were predicted by T1 need frustration negatively and positively, respectively. T2 academic buoyancy was predicted positively by T1 need satisfaction. The analysis also suggested a direct path (indicating a negative relation) from controlled motivation to students’ final grades. Finally, students’ T2 academic buoyancy mediated the relation between students’ need satisfaction or frustration and final achievement. Students’ need satisfaction as well as high autonomous and low controlled motivation could support students’ buoyancy and achievement in the normative settings of EPP. Training EPP teachers in supporting students’ psychological needs and enhancing their autonomous motivation seems to be important for strengthening students’ academic buoyancy and success in EPP.
Methodology, Methods, Research Instruments or Sources UsedIn this study, as the purpose was to investigate the relation of motivational experience at the beginning of a two-month course (T1) in EPPs to final levels (T2) of academic buoyancy and, through it, to achievement in the two-month course’s final exam (T3), a prospective research design was adopted. The T1 survey was completed by 443 students, while the T2 survey was completed by 310 students from three EPPs in Turkish English language medium universities. Among them 267 Turkish students participated both in T1 and T2. The students who participated in the study were selected according to the willingness of their teachers to provide class time to administer the survey. Two hundred and fifty-nine students were in their first year, and eight students were in their second year of the EPP. Data were collected through self-reports. The T1 survey assessed need satisfaction and frustrution, autonomous and controlled motivation and academic buoyancy in the second week of the English course of the third 8-week period in EPPs. T2 survey assessed students’ academic buoyancy in the seventh week of the English course. Each item in the questionnaires was assessed in a five-point, Likert-type scale ranging from 1 (totally disagree) to 5 (totally agree). Students’ need satisfaction and frustration were assessed by the Basic Psychological Need Satisfaction and Frustration Scale (BPNSFS; Chen et al., 2015; 12 items for need satisfaction, autonomy, a = .61; competence, a = .75; relatedness, a = .66; 12 items for need frustration, autonomy, a = .77; competence, a = .70; relatedness, a = .63). Sixteen items from the Academic Self-Regulation Questionnaire (SRQ-A; Ryan & Connell, 1989; autonomous motivation, a = .81, controlled motivation, a = .74) was used to assess students’ quality of motivation for their classwork in the course. The four-item Academic Buoyancy Scale (Martin & Marsh, 2008; α = .77) was used to measure the ability to overcome daily academic adversities in EPP. Students’ final exam scores in the English course were collected from the participated EPPs. As preliminary analyses, Cronbach alpha for each subscale was calculated and CFA to test the factor structure of all the measures was conducted using the R software with robust maximum likelihood estimation. The mean of each subscale was computed and the descriptive statistics and bivariate correlations were checked by using SPSS 20. Gender differences through MANOVA were also examined. In the main analyses, Structural Equation Modelling (SEM) was conducted using R software (package Lavaan) to test the hypotheses.
Conclusions, Expected Outcomes or FindingsThis study aimed to investigate, first, whether students’ academic buoyancy at the end of an English course in an EPP predicted by their initial motivational experience. Second, the study aimed to examine, to what extent students’ academic buoyancy at the end of an English course in an EPP mediate the relation between students’ initial motivational experience and final academic achievement. The findings suggested that when students perceived high need satisfaction in the EPP, they were also highly autonomously motivated. Alternatively, when they perceived need frustration, the quality of their motivation was less autonomous and more controlled. Moreover, autonomous and controlled motivation were mechanisms through which initial levels of need frustration in EPP were manifested to subsequent academic buoyancy. Interestingly enough, initial levels of need satisfaction and frustration in EPP were also directly related to subsequent academic buoyancy. Together, these two findings verify our initial argument that self-determined motivation (operationalized as need satisfaction and a sense of volition and personal causation, which is autonomous motivation) is also needed for students to be able to navigate the academic setbacks. Additionally, according to our predictions, high academic buoyancy at the end of the academic term was positively related to high final grades in the English course. Interestingly, apart from high academic buoyancy, low controlled motivation directly predicted high grades. Previous research in SDT has also shown that quality of motivation relates to academic achievement (Soenens & Vansteenkiste, 2005). The present study showed that students’ success in the normative settings of EPPs depended on both their quality of motivation and their ability to “float on academic water”. Moreover, students’ success in EPPs depended on need satisfaction as it was positively (and need frustration negatively) related to final grades through academic buoyancy (or controlled motivation).
ReferencesChen, B., Vansteenkiste, M., Beyers, W., Boone, L., Deci, E. L., Van der Kaap-Deeder, J., . . . Ryan, R. M. (2015). Basic psychological need satisfaction, need frustration, and need strength across four cultures. Motivation and Emotion, 39, 216–236. https://doi.org/10.1007/s11031-014-9450-1
Martin, A. J., & Marsh, H. W. (2006). Academic resilience and its psychological and educational correlates: A construct validity approach. Psychology in the Schools, 43, 267–281. https://doi.org/10.1002/pits.20149
Martin, A. J., & Marsh, H. W. (2008). Academic buoyancy: Towards an understanding of students' everyday academic resilience. Journal of School Psychology, 46(1), 53-83.
Ryan, R. M., & Connell, J. P. (1989). Perceived locus of causality and internalization: Examining reasons for acting in two domains. Journal of Personality and Social psychology, 57, 749–761. https://doi.org/10.1016/j.jsp.2007.01.002
Ryan, R. M., & Deci, E. L. (2017). Self-determination theory: Basic psychological needs in motivation, development, and wellness: Guilford Publications.
Soenens, B., & Vansteenkiste, M. (2005). Antecedents and outcomes of self-determination in 3 life domains: The role of parents’ and teachers’ autonomy support. Journal of Youth and Adolescence, 34, 589–604. https://doi.org/10.1007/s10964-005-8948-y
|
