Guest researchers and contributions at the Division of Empirical Educational Research

In this course, the foundations of (longitudinal) social network analysis will be covered. Participants will learn how they can set up a (longitudinal) social network study: theoretical background of social networks, research questions at different levels, data collection and ethical concerns, and understanding of the metrics.

The course learns participants to conceptualize and to set-up a social network study on their own. By practical assignments and in-depth discussions, the lecturer will support them in finding answers to the following questions: Why use social network analysis? What social network-research question to ask? How to get social network data? What are the options for analyzing social network data? The main assignment in this course is to prepare a short proposal within a group and present it at the end of the course. This can be assessed with a pass or fail for the course participants who need the credits. Each of the participants will write their reflection on their learning process and how they will use the course material in the future.

After finishing the course, the students are expected to:

• be able to explain when and why social network analysis should be applied rather than conventional statistical techniques
• be able to formulate research questions at different levels (node, dyadic, and network level)
• be able to design a social network study which can be used for their (master thesis/ PhD) project
• have insight in the ethical concerns related to social network research, e.g., data collection
• apply the content of the course to their own research project by asking critical questions and take part in discussions and assignments
• collaborate in small groups.

The student will pass the course (if the student needs the credits):

• if the student actively participates during the course
• presented the proposal and handed in the group presentation on time
• handed in the individual reflection of 800-1000 words on time. In the individual reflection the following question will be addressed: What did you learn from the course and how will you use this knowledge in your personal and professional life? The reflection can be based on own experience and two peer-reviewed references.

Introduction to SNA

Borgatti, S. P., Mehra, A., Brass, D. J., & Labianca, G. (2009). Network analysis in the social sciences. Science, 323, 892–895.

Kadushin, C. (2004). Introduction to Social Network Theory. Chapter 2. Some Basic Network Concepts and Propositions. Boston, MA.

Sweet, T. M. (2016). Social network methods for the educational and psychological sciences. Educational Psychologist, 51(3–4), 381–394.

Ego networks

Längler, M., Brouwer, J., Timmermans, A., & Gruber, H. (2021). Exploring change in networks supporting the deliberate practice of popular musicians. Psychology of Music, 20210419.

Illustration longitudinal SNA

Brouwer, J., de Matos Fernandes, C. A., Steglich, C. E. G., Jansen, E. P. W. A., Hofman, W. H. A., Flache, A. (2022). The development of peer networks and academic performance in learning communities in higher education. Learning and Instruction, 80. Open access link: doi.org/10.1016/j.learninstruc.2022.101603

Motivation is a complex construct that relates to individual factors like how much energy one devotes to assigned activities, the way one person thinks and feels about the activity and how long he or she remains involved in it. In addition, contextual factors like spending time with other learners or a good learning climate can play a crucial role in individuals’ motivation (for an overview on three main motivational perspectives regarding contextual factors within the domain of psychology see Urdan & Schoenfelder, 2006).

Investigating motivation using qualitative methods
The origins of motivation research were strongly tied to quantitative research methods since the founders of motivation research — Wallace Lambert, Robert Gardner, and their students and associates — were social psychologists trained within this research paradigm (for an overview see Dörnyei & Ushioda, 2013). Within quantitative research, surveys and experiments using questionnaires have been the dominant study methods (using cross-sectional and longitudinal study designs). In general, quantitative research is used to quantify the problem by generating numerical data or data that can be transformed into usable statistics. Regarding learning motivation, survey studies can analyze learners’ motivation in diverse geographical, sociocultural, and institutional contexts and compare the results of various subpopulations of learners. Experimental studies test if different motivational strategies increase student motivation, for example. Longitudinal studies can identify motivational changes at the individual and group level.
Qualitative research is often regarded as exploratory and is used to uncover trends in thoughts and opinions. Rich qualitative data describing the views and experiences of individuals regarding motivation can expand our understanding and provide empirical depth to the analysis of the phenomenon motivation. Phenomenology is a qualitative research method that focuses on the study of individuals’ lived experiences within the world (Neubauer et al., 2019; Teherani et al., 2015); if used in motivation study, it could investigate the nature of the experience, explore the phenomenon by examining it from the perspective of those who have experienced it, and outline the meaning of experience, both in terms of what was experienced and how it was experienced (Teherani et al., 2015).

University students’ motivation during the pandemic worldwide: examining rarely heard voices using the phenomenological approach
In general, demographic variables must be considered while analyzing motivation (for evidence from Sri Lanka see de Silva et al., 2018). However, most theories of motivation were developed in western, educated, industrialized, rich, and democratic (WEIRD) nations with specific demographic and cultural characteristics.
Studies on university students' motivation in WEIRD nations during remote learning and COVID-19 found that motivation decreased (for mixed methods research from USA see Aguilera-Hermida, 2020; for survey research from Canada see Daniels et al., 2021; for survey research from Netherlands see Meeter et al., 2020). The impact of COVID-19 on children and youth in low- and middle-income countries (LMICs) is more significant because of their living situations, socio-spatial contexts, and the pandemic's effect on essential social support networks such as caregivers, families, peers, and communities (Bong et al., 2020; Rahiem et al., 2021). Learning motivation also decreased among youth in LMICs in the pandemic, as shown by a phenomenological qualitative study in Bangladesh (Dutta & Smita, 2020), a mixed method study in India (Dhingra et al., 2021), and also a mixed method study in Vietnam (Van & Thi, 2021). However, not all students lost motivation due to online learning with COVID-19 (Lee et al., 2020; Rafique et al., 2021). In fact, my research in Indonesia in 2021 found that despite limitations and other obstacles, students were determined to continue their educations during these difficult times (Rahiem, 2021). This qualitative phenomenology study included eighty students from an Indonesian public university in Jakarta. Students created learning log diaries and reflective essays and participated in an online focus group discussion for data collection. This research method provides a platform for rarely or never heard voices. The findings are authentic, culturally bound, and address motivation in an understudied context.
In the workshop we will deal with the qualitative approach of phenomenology analyzing data about students’ motivation during the pandemic in Indonesia. A video about how students in Indonesia learned during a pandemic will be screened to provide an overview of the preceding study's context and to provide context for our exercise on the significance of social and cultural factors in the study of motivation. In addition, excerpts of the previous study (learning log diaries and reflective essays) will be shared, data interpretation will be practiced, and a cross-cultural dialogue is anticipated. You will also work on your own project using data collected by yourself.

Aguilera-Hermida, A. P. (2020). College students’ use and acceptance of emergency online learning due to COVID-19. International Journal of Educational Research Open, 1, 100011. doi.org/10.1016/j.ijedro.2020.100011

Bong, C.-L., Brasher, C., Chikumba, E., McDougall, R., Mellin-Olsen, J., & Enright, A. (2020). The COVID-19 Pandemic: Effects on Low- and Middle-Income Countries. Anesthesia and Analgesia, 131(1), 86–92. doi.org/10.1213/ANE.0000000000004846

Daniels, L. M., Goegan, L. D., & Parker, P. C. (2021). The impact of COVID-19 triggered changes to instruction and assessment on university students’ self-reported motivation, engagement and perceptions. Social Psychology of Education, 24(1), 299–318. doi.org/10.1007/s11218-021-09612-3

de Silva, A. D. A., Khatibi, A., & Ferdous Azam, S. M. (2018). Do the Demographic Differences Manifest in Motivation to Learn Science and Impact on Science Performance? Evidence from Sri Lanka. International Journal of Science and Mathematics Education, 16(1), 47–67. doi.org/10.1007/s10763-017-9846-y

Dhingra, S., Pasricha, N., Sthapak, E., & Bhatnagar, R. (2021). Assessing the Role of Internal Motivation and Extrinsic Factors on Online Undergraduate Medical Teaching in a Resource-Poor Setting During Covid-19 Pandemic in North India: An Observational Study. Advances in Medical Education and Practice, 12, 817–823. doi.org/10.2147/AMEP.S312812

Dörnyei, Z., & Ushioda, E. (2013). Teaching and researching: Motivation. Routledge.

Dutta, S., & Smita, M. K. (2020). The impact of COVID-19 pandemic on tertiary education in Bangladesh: students’ perspectives. Open Journal of Social Sciences, 8(09), 53.

Lee, J. X., Ahmad Azman, A. H., Ng, J. Y., & Ismail, N. A. S. (2020). Reflection of connectivism in medical education and learning motivation during COVID-19. MedRxiv, 2020.07.07.20147918. doi.org/10.1101/2020.07.07.20147918

Meeter, M., Bele, T., den Hartogh, C., Bakker, T., de Vries, R. E., & Plak, S. (2020). College students’ motivation and study results after COVID-19 stay-at-home orders.

Neubauer, B. E., Witkop, C. T., & Varpio, L. (2019). How phenomenology can help us learn from the experiences of others. Perspectives on Medical Education, 8(2), 90–97. doi.org/10.1007/s40037-019-0509-2

Rafique, G. M., Mahmood, K., Warraich, N. F., & Rehman, S. U. (2021). Readiness for Online Learning during COVID-19 pandemic: A survey of Pakistani LIS students. The Journal of Academic Librarianship, 47(3), 102346. doi.org

doi.org/10.1016/j.acalib.2021.102346

Rahiem, M. D. H. (2021). Remaining Motivated despite the Limitations: University Students’ Learning Propensity during the COVID-19 Pandemic. Children and Youth Services Review, 105802.

Rahiem, M. D. H., Krauss, S. E., & Ersing, R. (2021). Perceived Consequences of Extended Social Isolation on Mental Well-Being: Narratives from Indonesian University Students during the COVID-19 Pandemic. In International Journal of Environmental Research and Public Health (Vol. 18, Issue 19). doi.org/10.3390/ijerph181910489

Urdan, T., & Schoenfelder, E. (2006). Classroom effects on student motivation: Goal structures, social relationships, and competence beliefs. Journal of School Psychology, 44(5), 331–349. doi.org/10.1016/j.jsp.2006.04.003

Van, D. T. H., & Thi, H. H. Q. (2021). Student Barriers to Prospects of Online Learning in Vietnam in The Context of Covid-19 Pandemic. Turkish Online Journal of Distance Education, 22(3), 110–123.

When researchers have access to raw data from multiple studies (vs. solely effects sizes), then they may wish to use integrative data analysis (IDA; Curran & Hussong, 2009) instead of meta-analysis. Like meta-analysis, IDA requires that the independent and dependent variables be respectively similar across studies, preferably measuring the same underlying constructs. After aggregating or stacking the data, analyses—typically multiple regressions—are done at the case or person level instead of the study level. This allows for greater flexibility than meta-analysis, including the ability to test nonlinear and interactive effects (moderators). Like meta-analysis, one can also code for study-level differences (in design, participant demographics, etc.). IDA is especially useful for aggregating data across one’s own studies in multi-study papers because one often has the raw data and because IDA makes fewer implicit assumptions (e.g., linearity) than meta-analysis.   

Curran, P. J., & Hussong, A. M. (2009). Integrative data analysis: The simultaneous analysis of multiple data sets. Psychological Methods, 14(2), 81–100. https://doi.org/10.1037/a0015914

Webster, G. D., Smith, C. V., Brunell, A. B., Paddock, E. L., & Nezlek, J. B. (2017). Can Rosenberg’s (1965) Stability of Self Scale capture within-person self-esteem variability? Meta-analytic validity and test–retest reliability. Journal of Research in Personality, 69, 156–169. https://doi.org/10.1016/j.jrp.2016.06.005