01. Professional Learning and Development
Paper
Job Crafting and AI Resources - Introducing Sustainable Job Crafting Model
Niina Halonen, Terhi Susanna Nissinen
University of Helsinki, Finland
Presenting Author: Halonen, Niina
The increasing importance of workplace learning is evident as we continually encounter situations lacking predefined models, guidelines, interpretations, tools, or solutions. These complex scenarios demand immediate resolution within the workplace context (Harteis, 2022). However, constant changes and accelerating pace may induce stress and challenge mental well-being (Blomgren & Perhoniemi, 2022) if not addressed with appropriate tools and strategies (Hobfoll, 1989). The growing pressures on learning and skill development necessitate a re-evaluation of learning methods, practices, and techniques (Nissinen et al., 2022; 2023). The workplace is thus challenged to embrace flexible thinking and develop innovative tools for work transformation (Markauskaite & Goodyear, 2017). It is not just about surviving at work, but being able to feel a healthy enthusiasm and work engagement.
The most recent variable, Artificial Intelligence (AI), brings demands for rapid and flexible renewal in the work context (Halonen et. al., 2023). However, people may not have enough energy and resources for learning new things if they are constantly in a state of overburden (Knight et al., 2021). Additionally, the ways job crafting is done, can become burdensome and even threaten well-being at work (Nissinen et al., 2023). AI stands out from earlier technologies due to its capacity for (semi-)independent action (Maedche et al., 2019; Rieder et al., 2020; Scherer, 2016). Recent advancements in generative AI, notably the advancing sophistication of Large Language Models (LLMs), are enhancing the significance and adoption of AI-driven technologies in organizational contexts (Dwivedi et al., 2023; Markus and Rowe, 2023)
The role of artificial and supportive intelligences in workplace learning can be examined through system-theoretical lenses. Artificial Intelligence (AI) can be perceived as an integral system component, coexisting with human actors, essential for the collaborative creation of new knowledge. Consequently, AI can reshape the system (practices) and introduce novel inputs into discussions, which individuals or teams could not generate without technology (Halonen et al., 2023).
In job crafting interventions, the rapid evolution of technology is seen as a driving force for the continual acceleration of workplace learning (Van Wingerden et al., 2017). We use Job Demands-Resources Theory (Demerouti et al., 2001) in developing a sustainable job crafting model, where AI is utilized as a systemic resource to reshape and craft work practices. Our goal is to interrupt possible burdensome cycles at work and introduce a model which aims to decrease workload, increase job crafting, work engagement, well-being and professional networks. Our model combines job crafting strategies, AI and network crafting, and leverages research on job crafting intervention models (Knight et al., 2021; Roczniewska et al., 2023), particularly from the perspective of sustainable work practices. We recognize the agentic role of AI technologies which radically changes the flow of information and interactions. Our perspective of AI extends beyond merely accelerating tasks and supplying pre-formulated solutions. We envision it as a catalyst for novel types of network intelligence, stimulating collective engagement and provoking epistemic emotions that cultivate creativity, dedication, and elements of higher-level learning (problem solving, critical thinking, creativity) which are also crucial at workplace learning. We presented the issue at a National School Principal Conference in Helsinki, Finland in November 2023. Twenty of the conference participants informed us that they were interested in participating in the pilot of the sustainable job crafting model. We aim to gather max. 50 participants in this study.
Methodology, Methods, Research Instruments or Sources UsedTo test the hypotheses we will conduct two repeated self-evaluative measurements and multivariate analyses of covariance (MANCOVA). In measurements we utilize the job Crafting Scale to measure job crafting (Tims et al., 2012) and UWES-9 to measure work engagement (Schaufeli et al., 2006). We will also measure workload (van Veldhoven & Meijman, 1994) and we adopt measurement from Wang et al. (2024) to investigate network behavior. Pre-test and post-test also include semi-structured qualitative methods which strengthen the quantitative data, particularly in the use of AI.
Conclusions, Expected Outcomes or FindingsExpected outcomes: We hypothesize that 1) participants´ job crafting behavior increase via sustainable job crafting, 2) participants´ workload decrease via sustainable job crafting, 3) participants´ job engagement increase via sustainable job crafting, 4) participants increase their conscious use of AI in their own job and in collaborative processes, and 5) participants´ increase their network size and network diversity through the mediation of tailored network crafting actions (i.e. using existing contacts, establishing new contacts, maintaining professional contacts).
ReferencesAnthony, C., Bechky, B. A., & Fayard, A. L. (2023). “Collaborating” with AI: Taking a system view to explore the future of work. Organization Science.
Demerouti, E., Bakker, A. B., Nachreiner, F., & Schaufeli, W. B. (2001). The job demands-resources model of burnout. Journal of Applied Psychology, 86(3), 499-512. https://psycnet.apa.org/doi/10.1037/0021-9010.86.3.499
Halonen, N., Ståhle, P., Juuti, K., Paavola, S., & Lonka, K. (2023, September). Catalyst for co-construction: the role of AI-directed speech recognition technology in the self-organization of knowledge. In Frontiers in Education (Vol. 8, p. 1232423). Frontiers.
Knight, C., Tims, M., Gawke, J., & Parker, S. K. (2021). When do job crafting interventions work? The moderating roles of workload, intervention intensity, and participation. Journal of Vocational Behavior, 124, 103522. https://doi.org/10.1016/j.jvb.2020.103522
Markauskaite, L., & Goodyear, P. (2017). Epistemic fluency and professional education. Springer, Netherlands. https://doi.org/10.1007/978-94-007-4369-4 ISBN 978-94-007-4369-4 (eBook)
Nissinen, T. S., Maksniemi, E. I., Rothmann, S., & Lonka, K. M. (2022). Balancing work life: job crafting, work engagement, and workaholism in the finnish public sector. Frontiers in Psychology, 13, 817008. https://doi.org/10.3389/fpsyg.2022.817008
Nissinen, T. S., Upadyaya, K., Lammassaari, H., & Lonka, K. (2023). How Do Job Crafting Profiles Manifest Employees’ Work Engagement, Workaholism, and Epistemic Approach?. Vocations and Learning, 1-22. https://doi.org/10.1007/s12186-023-09334-x
Roczniewska, M., Rogala, A., Marszałek, M., Hasson, H., Bakker, A. B., & von Thiele Schwarz, U. (2023). Job crafting interventions: what works, for whom, why, and in which contexts? Research protocol for a systematic review with coincidence analysis. Systematic reviews, 12(1), 10. https://doi.org/10.1186/s13643-023-02170-z
Schaufeli, W. B., Bakker, A. B., & Salanova, M. (2006). The measurement of work engagement with a short questionnaire: A cross-national study. Educational and Psychological Measurement, 66(4), 701–716. https://doi.org/10.1177/0013164405282471
Tims, M., Bakker, A. B., & Derks, D. (2012). Development and validation of the job crafting scale. Journal of Vocational Behavior, 80(1), 173–186. https://doi.org/10.1016/j.jvb.2011.05.009
van Veldhoven, M. J. P. M., & Meijman, T. F. (1994). The measurement of psychosocial job demands with a questionnaire (VBBA). Amsterdam: NIA.
Wang, H., Demerouti, E., Rispens, S., & van Gool, P. (2023). Crafting networks: A self-training intervention. Journal of Vocational Behavior, 103956.https://doi.org/10.1016/j.jvb.2023.103956
van Wingerden, J., Bakker, A. B., & Derks, D. (2017). The longitudinal impact of a job crafting intervention. European Journal of Work and Organizational Psychology, 26(1), 107-119. https://doi.org/10.1080/1359432X.2016.1224233
van Wingerden, J., Bakker, A. B., & Derks, D. (2017). Fostering employee well-being via a job crafting intervention. Journal of Vocational Behavior, 100, 164-174. https://doi.org/10.1016/j.jvb.2017.03.008
01. Professional Learning and Development
Paper
Knowledge Domains in Blended Practice Teaching Settings: Grounding Theory in Practice
Lily Orland-Barak1, Alona Forkosh Baruch2, Ron Blonder3, Alexandra Danial-Saad1
1University of Haifa; 2Levinsky-Wingate Academic College; 3Weizmann Institute of Science
Presenting Author: Orland-Barak, Lily;
Forkosh Baruch, Alona
Objective and Theoretical Background
This multiple-layer, mixed methods research and development study set to identify the domains of knowledge that preservice students, their practice teachers and pedagogical advisors perceived as crucial for online teaching. This based the development and piloting of an evidence-based mentoring model of online teacher learning in practice.
Contemporary professional education (PE) in the digital era carries significant
implications for rethinking course design and curricula in teacher education. However, there are evident divergences between the content and teaching methods promoted in preservice programs and the demands of actual teaching (Reisoğlu & Çebi, 2020). Hence, the need to develop teacher education curricula with a focus on professional, context-based, contemporary, knowledge construction (Bradbury et al., 2015; Wang & Orland-Barak, 2020), based on relevant practice in virtual spaces (Yuan, 2018).
While the reality is that online teaching and learning has developed into an alternative for face-to-face teaching and learning, there is evidence regarding faculty beliefs, stating that online learning outcomes may be poorer compared to face-to-face settings (Ward & Benson, 2010). This may be due to the lack of balance in content and pedagogical practices, which may create an overload in both teaching and learning (James et al., 2021). Moreover, faculty beliefs are not taken into account, while- according to studies-they are indeed factors that affect utilization of technology in learning in general and in online learning in particular.
When referring to online teaching and learning, technology has a remarkable influence which may have either positive or negative impact on learners’ experiences and outcomes (Panigrahi et al., 2018). These experiences are unique, allowing learning processes that may be impossible to achieve otherwise. Placing technology alongside pedagogy and content reflects the complexities of online learning and creates an effective prism for examining the needs of online teaching (Eichelberger & Leong, 2019).
Due to the increasing usage of online modes of teaching in higher education, implementing them in teacher education programs may be a lever for innovative teaching and learning, especially with the expanding tendencies of online education worldwide, as a result of contemporary events, and despite its complexities (Isaias et al., 2020; Martin et al., 2020). However, while digital competencies are growingly incorporated in preservice teachers’ curricula, there seem to be complexities supporting this tendency (Tømte et al., 2015), either due to a lack of self-efficacy (Ding & Hong, 2023) and digital competencies (Marais, 2023), or the slow process of transforming teacher educators’ curriculum so they themselves integrate technology (Voithofer, 2021), to name a few.
We address the challenge of shifting to blended teaching and teacher education in an attempt to link preservice teacher practice to developing trends in teaching and learning. Moreover, we acknowledge the vagueness and insecurity of teacher educators, who are in fact mediating professional knowledge of the “old world” of teaching, and may not be proficient in training preservice teachers, as experienced in times of emergency remote teaching (Trust & Whalen, 2020). The current study tackles this incongruence by identifying and applying domains of knowledge needed for quality online teaching.
Research question: What domains of knowledge characterize the digital teacher learning space? and how can these be translated into a model for mentoring in the blended teacher learning space?
Methodology, Methods, Research Instruments or Sources UsedMethods
This mixed-methods study draws on qualitative and quantitative methodologies, applied in a sequencial manner according to which each stage was built on previous stages:
1. We constructed focus groups (N=7) of a total of 14 interviewees, including (separately) preservice teachers, mentor teachers, teacher educators, policymakers, position holders in teacher training programs and researchers in the field of technology in education. Content analysis which combined emic and etic perspectives was applied to the transcribed interviews. This constituted the basis for developing and piloting an evidence-based professional development mentoring model for preservice teachers geared to improving their online teaching skills.
2. Content analyis resulted in the consolidation of five 5 knowledge domains which were translated into 5 operative questions, representing what is required for best online teaching: 1. How to engage students towards learning; 2. How to monitor students’ learning; 3. How to create interactions and communication for learning; 4. How to retool content; 5. How to develop digital literacy of students. These based the construction of an online model for practice of preservice teachers in online settings
3. For each question (representing a domain) we developed a module based on examples from the interviews which assisted in phrasing authentic teaching vignettes. The modules included: an abstract, theoretical perspectives, references, objectives, teaching scenarios, followed by activities and reflective tasks. The modules were constructed in an open, modular manner, to allow flexibility, new ideas and activities gained from participants ‘in-situ’ experiences. These will be presented.
4. Through design-based methodology we conducted a pilot study with teacher educators (N=19). We created a laboratory for examining, applying the modules in their preservice teaching, and refining these modules for online learning. Participants freely registered and gave feedback according to their training experience with preservice teachers.
5. The quantitative component of the research included a pre- (N=19) and post (N=12) questionnaire that was validated and distributed to teacher educators regarding their practical and perceived experience with the bodies of knowledge they were exposed to in the laboratory and as a result of applying the modules. A similar questionaire adapted to preservice teachers was also distributed (n=94 for the research group that were taught by the teacher educators participating in the laboratory, and n=67 for the control group that experienced standard training). Analysis was conducted using descriptive and inferential statistics.
Conclusions, Expected Outcomes or Findings6. Findings and Conclusions
1) Same domains-different concerns
The knowledge domains characterizing the digital teaching space were reinforced in our questionnaire results, both by teacher educators and preservice teachers. They could also be associated , to a large extent, with the knowledge domains that characterize face-to-face teaching, except for monitoring and developing digital literacies, which was seen as vital to applying best online practices. Although similar, however, participants reported on different kinds of concerns around these knowledge domains when teaching on-line. These discrepancies will be elaborated and illustrated in the paper presentation.
2) The laboratory as a platform for curriculum and teacher development
The laboratory, which focused on participants’ development of the constructed modules was found to enable deep collaborative contemplation into online teaching through theorizing, reconstructing and transforming teaching practices in their online teaching and learning settings. We also learned that its structure should be flexible in terms of time, pre-planning, adaptable to participants’ practical needs. Our study suggests that utilizing the modules impacted teacher educators, their preservice teachers and the students in their practical training, foregrounding major challenges of teacher education practices that had until now been backgrounded. For example, while we assume that preservice teachers are already better accustomed to the digital era, they are not yet skilled in online teaching, and much of the teaching online expertise requires re-evaluation of familiar knowledge domains , e.g., how to communicate using multiple channels or how to monitor student engagement. Furthermore, even when addressing digital literacy, the usual “how to” is of less concern to educators than, for example, emergent ethical issues related to working online.
ReferencesReferences
Bradbury, H., Kilminster, S., O'Rourke, R., & Zukas, M. (2015). Professionalism and practice: critical understandings of professional learning and education. Studies in Continuing Education, 37(2), 125-130.
Ding, L., & Hong, Z. (2023). On the relationship between pre-service teachers’ sense of self-efficacy and emotions in the integration of technology in their teacher developmental programs. The Asia-Pacific Education Researcher. https://doi.org/10.1007/s40299-023-00758-6
Eichelberger, A., & Leong, P. (2019). Using TPACK as a framework to study the influence of college faculty’s beliefs on online teaching. Educational Media International, 56(2), 116-133.
Isaias, P., Sampson, D.G., & Ifenthaler, D. (Eds.). (2020). Online teaching and learning in higher education. Springer International Publishing.
James, T.L., Zhang, J., Li, H., Ziegelmayer, J.L., & Villacis-Calderon, E.D. (2021). The moderating effect of technology overload on the ability of online learning to meet students' basic psychological needs. Information Technology & People, 35(4), 1364-1382.
Marais, E. (2023). The Development of Digital Competencies in Pre-Service Teachers. Research in Social Sciences and Technology, 8(3), 134-154.
Martin, F., Sun, T., & Westine, C. D. (2020). A systematic review of research on online teaching and learning from 2009 to 2018. Computers & education, 159.
Orland-Barak, L., & Wang, J. (2020). Teacher mentoring in service of preservice teachers’ learning to teach: Conceptual bases, characteristics, and challenges for teacher education reform. Journal of Teacher Education, 35(6), 42-55.
Panigrahi, R., Srivastava, P.R., & Sharma, D. (2018). Online learning: Adoption, continuance, and learning outcome—A review of literature. International Journal of Information Management, 43.
Reisoğlu, İ., & Çebi, A. (2020). How can the digital competences of pre-service teachers be developed? Examining a case study through the lens of DigComp and DigCompEdu. Computers & Education, 156.
Tømte, C., Enochsson, A.B., Buskqvist, U., & Kårstein, A. (2015). Educating online student teachers to master professional digital competence: The TPACK-framework goes online. Computers & Education, 84, 26-35.
Trust, T., & Whalen, J. (2020). Should Teachers Be Trained in Emergency Remote Teaching? Lessons Learned from the COVID-19 Pandemic. Journal of Technology and Teacher Education, 28(2), 189–199.
Voithofer, R., & Nelson, M.J. (2021). Teacher educator technology integration preparation practices around TPACK in the United States. Journal of teacher education, 72(3), 314-328.
Ward, C.L., & Benson, S.K. (2010). Developing new schemas for online teaching and learning: TPACK. MERLOT Journal of Online Learning and Teaching, 6(2), 482-490.
Yuan, H. (2018). Preparing teachers for diversity: A literature review and implications from community-based teacher education. Higher Education Studies, 8(1), 9-17.
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