Promotion focus, but not prevention focus of teachers and students matters when shifting towards technology-based instruction in schools

Students are in a different situation than teachers since they typically are not to decide whether to use technology when it is introduced into their daily classroom teaching practices. The literature mainly reports positive attitudes of students towards technology in the classroom^14,45. Nevertheless, students’ attitudes toward technology and openness toward accepting this change in teaching may still affect their learning behavior, engagement, and motivation to learn.

In a broad sense, motivation is defined as being moved to do something which, consequently, gives rise to an action⁴⁶. It can be specified as a preference toward specific activities, objects, or events and refers to liking, value, and interest in a task or activity. Research showed that, in general, interest is associated with improved learning^47,48; a recent meta-analysis confirmed interest as a predictor of academic achievement⁴⁹. Regarding technology, in particular, it has been shown that students’ attitudes toward technology and how they perceive educational technology drive their intention and actual use of it^50,51.

How well students think they can use technology may as well be related to their learning behaviors in technology-enhanced teaching. Self-efficacy beliefs refer to beliefs in one’s abilities to perform a given behavior⁵², that is, individuals’ conviction that they can successfully execute behaviors necessary to produce specific performance outcomes. Evidence shows that self-efficacy is associated with academic achievement and performance^53,54,55,56. Students’ self-efficacy in using technology has been shown to relate to their computer and information literacy^57,58 and to better grades in online courses⁵⁹. Thus, understanding motivation in educational settings is highly relevant because of the consequences for learning behavior and its relation to student achievement^54,60,61.

The regulatory focus theory describes motivational consequences that may arise from how individuals cope with novel situations. Given that promotion and prevention focus imply preferences for certain types of situations or tasks—i.e., individuals with a stronger promotion focus like to try out new things, and individuals with a stronger prevention focus prefer to follow given rules⁶—research has studied the influence of the fit between the implied preferences of regulatory orientation on the one hand and the specific task on the other hand. According to the regulatory fit hypothesis^20,62, engagement and motivation are stronger when a situation, event, or behavior fits an individual’s regulatory orientation. In addition, the situation, objects, and people an individual interacts with will be perceived more positively in case of a high regulatory fit^63,64. Further, high regulatory fit goes along with higher engagement and effort for fitting tasks⁶⁵. For example, a person with a promotion focus will feel more positively and engage more strongly when writing an essay on a topic of one’s own choice than when writing dictation because the essay task allows one to make more use of the preferred strategy. On the other hand, a person with a prevention focus will feel more positively and engage more strongly with the dictation because it fits the preference to follow given rules.

As described above, technology-supported teaching is characterized by more individualized and student-centered pedagogies where students take a more active and self-regulated role in their learning^1,2,14,16,18. Such an environment is characterized by more degrees of freedom for students and less tight teacher-directed instructional practices. Therefore, it can be assumed that students’ strength of engagement and motivation differs depending on the fit of technology-enhanced teaching and learning situation to students’ regulatory orientation.

In the current research, we aim to investigate whether students’ regulatory orientation relates to their motivational outcomes in the context of introducing technology in the classroom. For this purpose, we used longitudinal data from students from the school trial in which 7^th-grade students were equipped with tablet computers on a one-to-one basis. Specifically, we examined whether students’ regulatory orientation measured prior to handing out the tablet computers at the beginning of the school term is associated with motivational variables (i.e., their perception of technology, technology-related self-efficacy, and motivation to use technology) assessed at the end of the school term. In particular, we expected students with a strong promotion focus to respond more positively to the experience of learning with tablet computers in the classroom than those with a high prevention focus for at least two reasons. First, they are expected to be more open to change; second, the situation better fits their regulatory orientation. Thus, after having worked with the tablet computers in class for some months, we expected students with a stronger promotion focus to perceive technology more positively (hypothesis 1a), to have higher technology-related self-efficacy (hypothesis 2a), and to be more motivated to use technology (hypothesis 3a). In turn, we expected no or even negative effects on motivational outcomes for students with a stronger prevention focus due to their preference for well-known grounds and low fit to the situation. Thus, after having worked with the tablet computers in class for some months, we expected students with a stronger prevention focus to perceive technology less positively (hypothesis 1b), to have lower technology-related self-efficacy (hypothesis 2b), and to be less motivated to use technology (hypothesis 3b).

Method

Context of the study

Data stem from the same school trial as Study 1. Study 2 refers to data from students at the time when being equipped with tablet computers (t₀, i.e., at the end of the first school semester of 7^th grade, which was in spring 2018 and 2019 in cohort 1 and 2, respectively) and approximately four to five months later (t₁, i.e., at the end of the second school semester of 7^th grade, which was in summer 2018 and 2019 in cohort 1 and 2, respectively).

Participants

A total of 1,379 students from tablet classes participated in the data collections of the overall project. After applying our exclusion criteria (for student participant flow, see Fig. 2), the sample we analyzed comprised data from N = 1,127 students (548 female, 577 male, two students did not provide their gender) at the age of 11 to 17 years (M = 12.49 years, SD = 0.59, four students did not provide their age). The sample comprised 1,020 complete cases, 78 cases that participated only at t₀, and 29 cases that participated only at t₁.

Student participant flowchart. (^aStudents of these classes were excluded because the change we were interested in had already taken place in these schools; the exclusion did not change the pattern of results.)

Measures

All items of the students’ questionnaire scales relevant to this study are listed in Appendix B.

Regulatory orientation was assessed as promotion focus (Cronbach’s α = 0.77, e.g., “I want to achieve a great deal in my life.”) and prevention focus (Cronbach’s α = 0.71, e.g., “In case of important decisions, security is important to me.”), each being measured with six items at baseline (t₀). These items were adapted from the scale used in Study 1³⁴, originally designed for adults. Items suitable for students were selected, and some items were adapted to better fit students’ lives in school (e.g., “Success sets me at ease.” from the original scale was changed to “If I receive a good grade, it reassures me.”). All items were measured on a 5-point scale from (1) strongly disagree to (5) strongly agree.

Perception of technology (10 items of which five items were reversely coded; Cronbach’s α = 0.76; e.g., “I like to use digital devices.”), technology-related self-efficacy (7 items of which one item was reversely coded; Cronbach’s α = 0.82; e.g., “If a problem arises with a digital device, I think I can solve it.”), and motivation to use technology (7 items; Cronbach’s α = 0.75; e.g., “I like to familiarize myself with new digital devices.”; motivation at t₀ was mistakenly assessed on a 5-point scale; thus, for the purpose of this study, the values of each item were rescaled to a 4-point scale.) were each measured on 4-point scales ranging from (1) not at all to (4) very much (adapted and extended from the scales interest in ICT, personally perceived ICT competence, and perceived autonomy in ICT use from ⁶⁶). The three scales were measured at both measurement points, t₀ and t₁. For the sake of brevity, these variables hereafter are referred to as perception, self-efficacy, and motivation, respectively.

Procedure

Trained research assistants conducted data collection in the classrooms at each measurement point. Given that consent was provided by the students and their legal guardians, the students completed the questionnaires online on their tablets. The self-report questionnaires comprised questions on how students worked with the tablet computers (e.g., frequency of use per subject), how they perceived their learning environment in classes with and without tablet computers (e.g., perceived teaching quality), and students’ backgrounds (e.g., demographics). Furthermore, paper–pencil instruments tested students’ abilities (e.g., cognitive abilities). The scales relevant to the current research (i.e., regulatory orientation, perception of technology, technology-related self-efficacy, motivation to use technology) were all assessed in the online self-report questionnaire. Rather stable variables were only assessed at t₀ (e.g., personality), whereas variables expected to vary over time were assessed at each measurement point (e.g., perception of digital media). At t_0, data collection took 180 min and was divided into two sessions of 90 min each. Each following measurement point lasted 90 min.

Results

Again, the analyses were conducted with the statistical software R version 4.3.1⁴¹ and the lavaan package version 0.6–15⁴². Descriptive values (means and standard deviations) and correlations of all study variables are reported in Table 3.

We investigated the hypotheses that students’ promotion focus at the time they got the tablet computers (t₀) would be positively associated with their perception, self-efficacy, and motivation at the end of the school year (t₁; hypothesis 1a, 2a, 3a) and that students’ prevention focus at the time they got the tablet computers (t₀) would be negatively associated with their perception, self-efficacy, and motivation at the end of the school year (t₁; hypothesis 1b, 2b, 3b). To this end, we fitted a regression model (Table 4) to examine the effects of promotion focus and prevention focus both at t₀ on students’ perception, self-efficacy, and motivation each at t₁ while controlling for the student’s perception, self-efficacy, and motivation at t₀, respectively, as well as for age and gender (female = 0, male = 1). Interaction terms were not included in the model. The parameters were calculated using maximum likelihood estimation using the Yuan-Bentler correction and cluster robust standard errors with class as cluster variable⁴³. Further, we estimated the variances and covariances of the exogenous variables in the model to account for these variables when estimating missing values in the full information maximum likelihood procedures for missing data.

RQ: Is students’ regulatory orientation related to their motivational outcomes in the context of introducing technology-enhanced teaching?

In line with expectations, the results (see Table 4) showed a positive association of students’ baseline promotion focus (t₀) with their self-efficacy (hypothesis 2a) and motivation (hypothesis 3a) after working with tablet computers in school for some months (t₁). Other than expected, however, this relation was not observed for students’ perception of technology (hypothesis 1a). Further, students’ prevention focus at baseline neither showed a relation to their perception (hypothesis 1b), self-efficacy (hypothesis 2b), or motivation (hypothesis 3b) after working with tablet computers in school for some months (t₁).

Further, the model showed significant differences for gender. After having worked with tablet computers in school for some months, male compared with female students perceived technology less positively, but had higher self-efficacy beliefs, and were more motivated to use technology.

Discussion

Study 2 shed light on how students dealt with changing teaching practices from switching to technology-enhanced teaching and learning. It supported our hypothesis that students’ promotion focus was associated with motivational consequences of introducing tablet computers into the classroom. In contrast and similar to the teachers’ results, students’ prevention focus did not play a role. Thus, the results can be considered as further evidence of the relevance of individuals’ promotion focus when introducing technology in classrooms and that prevention focus is of minor relevance.

It should be noted, however, that we examined motivational outcomes related to technology as a dependent measure. That students feel more competent in using technology (stronger self-efficacy beliefs) or are more motivated to use technology is just a first step for successful technology integration. As reported earlier, motivational variables are closely related to other outcome measures^58,60. However, future research should investigate whether more favorable motivational conditions induced by promotion focus are then associated with high-quality technology use and higher learning achievement.

Inconsistent with the other findings of teachers’ or students’ promotion focus, there was no relation between students’ baseline promotion focus and later perception of technology. However, gender effects also appeared in the opposite direction for perception compared to the gender effects of the other criterion variables, self-efficacy and motivation. Thus, it remains to be investigated in further research whether student promotion focus is indeed unrelated to the perception of digital media or whether the inconsistent pattern of findings was caused, for example, by difficulties in measuring students’ perception.