The accelerating rise and widespread adaptation of digital technology in private and business sectors has led to a European consensus in regards to the necessity of the regular integration of technology in educational settings in order to enhance learning in general and prepare students for a competent use of digital technology (Peña-López, 2015). Due to the COVID-19 pandemic, the process has been accelerated even more – especially in European regions (Helm, Huber & Loisinger, 2021). One coping strategy that was adapted broadly in most educational institutions in Europe and beyond was emergency remote teaching (ERT), which shifted presence learning to online learning (Bozkurt & Sharma, 2020). The implementation of ERT in European regions proved to be rather diverse, e.g., with Portugal even using their television channels to cope with the pandemic (Seabra et al., 2021). In higher education, this shift towards online learning has proven to be emotionally challenging for learners – especially for pre-service teachers, with technology attitudes as primary influences (Schneider et al., 2021).

Referring to the elaboration of Tellegen et al. (1999) on the Circumplex Model of Affect, in which positive activation comprises positively valued states such as “enthusiastic” and negative activation comprises negative valued states such as “distressed”, emotional challenge arises either due to a decline in positive activation or an incline in negative activation as changes in emotional state. On a behavioral level, positive activation entails approaching behavior and negative activation avoidant behavior (Watson, 1999). Provided that attitudes are dispositions to respond favorably or unfavorably towards something (Ajzen, 2005), technology attitudes are closely related to positive and negative activation in the context of using technology. Therefore, the emotional state after ERT and the perception of their study experience in the transition away from ERT is expected to change in a more positive or negative direction depending on the underlying attitudes.

A more general approach to a person’s relationship with technology is due to the construct technology commitment. Neyer et al. (2012) conceptualize technology commitment as three dimensional: technology acceptance (referring to the technology attitudes from the Technology Acceptance Model), technology competence (operationalized by the anxiety to use technology), and technology control (as in the specifically technology related locus of control construct). Extensive research across the globe shows that technology commitment predicts the use of technology (Scherer et al., 2019) and emotional state whilst frequently using it (Händel et al., 2020; Schneider et al., 2021). Recent research indicates that the relationship between technology commitment and emotional state differs between clusters of technology commitment for in-service teachers (Pozas et al., 2022). Thus, it remains to be examined if this also holds for pre-service teachers. In summary, the following research questions will be addressed in this contribution:

1. What technology commitment profiles exist among pre-service teachers?
2. How do their emotional states whilst and after ERT differ in comparison?

The main objective of the research to be presented is to understand the interplay between technology commitment, emotional state and the study perspective of pre-service teachers in order to provide proper grounds for European practitioners to properly support pre-service teachers throughout their course of studies in a digital world.

To examine the research questions and to contribute to the main objective, data from a cohort study design is used in which a sample of pre-service teachers is enrolled in a teacher education (TE) program in Rhineland-Palatinate (Germany) and monitored. The monitoring project (TrigiKOM’MON) started in 2019 and is ongoing for the observation of digital competences and attitudes over the course of their Bachelor of Education (approximate monitoring time frame of 2.5 years for each cohort).

The data contains cohorts that started in different stages of the pandemic from pre-pandemic to today. It consists of four measurements during the Bachelor’s program and will approximately cover a time period of about four years at the end of 2023. Technology readiness data for examining the cluster structure (RQ1) included 969 student teachers having completed the respective scales near the end of their first year in TE. In this sample, proportion of females was 69.66%, mean age was 20.5 years (± 3.05). In examining RQ2, the subjects (N = 128) reported their emotional state on two occasions after their first year (summer term 2021 and winter term 2021/22; 71.9% female, 20.9 ± 1.4 years old). Teachers’ technology commitment was measured using the according Technology Commitment Questionnaire (TCQ) from Neyer et al. (2012). The aforementioned subscales are operationalized as followed: technology acceptance (e. g., “I am very curious when it comes to new technology developments”; α = 0.83), technology competence (e. g., “I have often fear to fail when dealing with modern technology”; α = 0.85), and technology control (e. g., “It depends essentially on me whether I am successful using modern technology”; α = 0.72). All sub-scales are based on a 5-point Likert scale ranging from 1 = strongly disagree to 5 = strongly agree. To assess teachers’ emotional state during and after ERT, the Positive and Negative Activation and Valence (PANAVA) short scales from Schallberger (2005) were administered: positive activation (PA; α = 0.76) and negative activation (NA; α = 0.65). The PA and NA comprise four bipolar items, respectively, rated on a 6-point Likert scale. Thus, the participants were asked to describe the experience of their current study situation within spectrums between different adjective pairs (e. g., “listless vs. motivated”). To explore the first research question, a series of cluster analyses will be conducted on the TCQ subscales, beginning with applying a single-linkage clustering algorithm to identify and exclude outliers. Results from subsequent Ward’s method clustering will then be cross-validated by k-means clustering. The second and the third research question will be examined with two-way ANOVAs (IV = clusters and study progress; DV = change in experience and emotional state).

Concerning the first research question, analyses are expected to yield three clusters in line with Pozas et al. (2022): (1) overall low to average technology commitment on all subscales; (2) mediocre technology commitment with technology competence as the highest subscale score; (3) overall high technology commitment on all subscales. If this pattern was to be found in TE, this might indicate an urgent need for interventions to help student teachers pertaining to cluster (1) to become motivated and competent in the use of technology. Furthermore, student teachers in cluster (2) are likely to overestimate themselves in their technology competence and thus are harder to identify for interventions that are also suited for cluster (1). Cluster (3) could serve as a potential resource for mentoring programs to facilitate Technology Commitment in clusters (1) and (2). With regard to the second research question, the extrapolation of the results from Schneider et al. (2021) and Pozas et al. (2022) suggests that the pre-service teachers with higher Technology Commitment scores would be emotionally more resilient to the ERT circumstances and also recover faster from the negative impacts of ERT. For teacher education, this could imply that technology commitment is a worthy subject to facilitate as a factor for resilience concerning future ERT scenarios and future technological challenges in general. The results and their implications will be discussed with the aim to optimizer teacher education accordingly. Additionally, at Trier University, there is a voluntary education program for pre-service teachers as an intervention which aims to prepare them for digital challenges. First post-measurements and thus results will be available and prepared as a basis to discuss approaches to support technology commitment.

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