Uncovering the Relationship Between Technology-Enhanced, Adaptive Teaching and Situational Interest in Mathematics in a Randomized Controlled Trial

doi:10.21203/rs.3.rs-5238796/v1

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Uncovering the Relationship Between Technology-Enhanced, Adaptive Teaching and Situational Interest in Mathematics in a Randomized Controlled Trial

https://doi.org/10.21203/rs.3.rs-5238796/v1

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Previous research has shown that students’ interest typically declines across secondary school, likely resulting from a mismatch between their needs and the school environment. Technology-enhanced adaptive teaching may allow teachers to better tailor their instruction to students’ needs, including students’ interests; thus, this approach may be promising in this regard. The objective of this study was to gain insight into the associations between equipping students and their teachers with tablet computers (1:1 technology), student-perceived adaptive teaching, and students' situational interest. We used questionnaire data (i.e., from two measurement points: t₀ = baseline; t₁ = 4 months later) from the longitudinal project tabletBW meets science from 2,601 students (Grade 7, Gymnasium, Germany) attending 28 schools. Fourteen schools were randomly chosen to be given 1:1 technology (i.e., tablet computers for teachers and students; intervention condition). The other 14 randomly chosen schools were not given such equipment (control condition). We assessed how students' situational interest in mathematics was associated with the use of tablet computers (intervention vs. control) and student-perceived adaptive teaching. Results from multilevel models showed that the 1:1 technology had a statistically significant effect on students' situational interest, partly mediated by student-perceived adaptive teaching. Moreover, knowing that students' interest in math declines in adolescence, our results indicate that equipping classrooms with technology might offer a promising way to implement more adaptive lessons that have the potential to provide the appropriate degree of challenge to students and thus spark their interest in mathematics.

Scientific community and society/Social sciences/Education

Scientific community and society/Social sciences/Psychology

adaptive teaching

technology-enhanced teaching

tablet computers

randomized controlled field trial

situational interest

Mathematics is a subject that students commonly dislike in school. Moreover, several studies have identified that students' interest in mathematics declines across the secondary school years (e.g., across Grades 5 to 9; Frenzel et al., 2010).[1] This decline is highly problematic given that students' interest in school subjects (e.g., mathematics) is positively associated with their academic achievement (Köller et al., 2001; Kriegbaum et al., 2018; Schiefele et al., 1992). Because citizens need mathematics to successfully navigate through today's society, as many everyday tasks require people to solve complex problems, it is critical to promote students' interest in mathematics.

The decline in students’ interest during secondary school may be the result of a mismatch between their needs and their learning environment (Eccles, 2004). There is reason to believe that students develop and maintain more interest in a subject when the difficulty level of a lesson is appropriate for a student’s skill level, that is, when they are not bored or overwhelmed (Feldon et al., 2019; Grund et al., 2024). In adaptive teaching, teachers tailor their instruction to students’ learning needs by paying close attention to each student's level of understanding and interests and adjusting their teaching accordingly (Corno, 2008; Corno & Snow, 1986). The goal is for each student to learn in their zone of proximal development (Vygotsky, 1979). Adaptive teaching may thus promote student engagement and interest in corresponding content domains (Parsons et al., 2018) and halt the decline in students' interest.

A crucial and simultaneously challenging aspect of adaptive teaching is the need for ongoing evaluations of students' learning needs (Hidi & Harackiewicz, 2000; Tetzlaff et al., 2020). Moreover, a teacher who adapts their instruction to individual students in a classroom must still manage all the students simultaneously, thus rendering teaching inherently more complex (Corno, 2008). Not surprisingly, recent studies have shown that teachers typically face problems in applying diagnostic strategies throughout their lessons and in maintaining adaptive teaching (e.g., Hardy et al., 2022). Technology offers a promising approach for supporting teachers in these challenges (Aleven et al., 2017). For this reason, among others, classrooms are more frequently coming equipped with 1:1 technology (or one-to-one computing), which refers to an educational program in which each student is given their own learning device. These devices are often tablet computers, which can be loaded with software programs that help teachers individualize learning tasks and provide adaptive feedback (Aleven et al., 2017; Plass & Pawar, 2020).

However, although schools are increasingly being equipped with digital devices such as tablet computers, evidence that tablet computers can be used to promote adaptive teaching in diverse classrooms is lacking. Thus, in this randomized controlled trial, we systematically investigated whether and how1:1 technology—specifically tablet computers—affects students' situational interest in mathematics and how this effect is related to student-perceived adaptive teaching.

[1] The term “secondary school” varies between countries; for instance, in the US, it typically includes Grades 6-12 or 7-12 depending on the presence of a middle or junior high school, while in the UK, it generally covers Years 7-11, and in Germany, it spans Grades 5-12 or 5-13, with Grades 5-10 known as “Sekundarstufe I” and Grades 11-12/13 as “Sekundarstufe II.”

Students’ Situational Interest

Interest is an essential construct that shapes people's lives by affecting their leisure activities or determining their career paths after high school graduation (Harackiewicz & Hulleman, 2010). In educational contexts, when students find a topic or activity interesting, they show more focused attention and increased cognitive functioning, which may lead to a deeper engagement and, finally, to enhanced learning outcomes and a more positive attitude toward the subject matter (Harackiewicz & Hulleman, 2010; Hidi, 2001; Jansen et al., 2016; Köller et al., 2001; Kriegbaum et al., 2018; Mitchell, 1993; Murayama, 2022; Rotgans & Schmidt, 2017b; Trautwein et al., 2006). In a meta-analysis, Schiefele et al. (1992) demonstrated that students’ interest in mathematics was positively associated with their mathematics achievement (r = .32, k = 31, SD = .086).

Interest is commonly described as a positive psychological state that drives individuals to engage with specific content or activities (Ainley & Hidi, 2014; Frenzel et al., 2010; Hidi & Renninger, 2006; Krapp et al., 1992; Schraw & Lehman, 2001). It is considered a "prototypical intrinsic motivational construct" (Harackiewicz & Knogler, 2017, p. 335) that is closely related to a feeling of enjoyment (Jung et al., 2024). In educational contexts, interest is assumed to be specific to a particular subject matter (Schiefele, 1991). Interest can be theoretically and empirically categorized into individual (or personal) interest and situational interest (Mitchell, 1993; Schraw & Lehman, 2001). Individual interest is a relatively long-lasting, enduring disposition toward a particular topic or activity, often resulting from a personal affinity or background knowledge (Hidi & Renninger, 2006). In contrast to individual interest, situational interest is a more transient and context-specific form of interest, often triggered by environmental stimuli or specific learning activities (Schraw & Lehman, 2001).

The relationship between these two ways of conceptualizing interest lies in the genesis of interest. Situational interest can contribute to the development of long-lasting individual interests (e.g., through affective-cognitive synthesis; Hidi & Harackiewicz, 2000). A theoretical framework that demonstrates how situational interest can evolve into individual interest is the four-phase model of interest development proposed by Hidi and Renninger (2006; see also Knogler et al., 2015; Renninger & Su, 2012). The basic assumption in the framework is that interest emerges from an individual's interaction with the environment (Hidi & Renninger, 2006; Krapp, 1999; Krapp et al., 1992; Murayama, 2022). According to the framework, situational interest can be triggered initially by capturing a student's attention via certain stimuli or activities that are novel, surprising, or personally relevant (triggered situational interest). The triggered interest needs to be stabilized through supportive conditions, such as instruction that promotes engagement and provides meaningful tasks (maintained situational interest). With continued exposure and positive experiences, situational interest can transform into emerging individual interest, characterized by a more persistent and deeper engagement (emerging individual interest), and finally develop into well-developed individual interest, where students show consistent and self-motivated engagement with the subject matter (well-developed individual interest).

Decades of research have shown that students' interest tends to wane as they progress through secondary school (Eccles & Midgley, 1989; Kunter et al., 2007; Scherrer et al., 2020; Wigfield et al., 2006). For instance, using longitudinal data from N = 3,193 students (51% female), Frenzel et al. (2010) showed a downward trend in students' interest in mathematics from Grades 5 to 9. Mathematics appears to be particularly vulnerable to such waning interest (Jacobs et al., 2002; Köller et al., 2001; Watt, 2004; for a recent example, see Lehikoinen et al., 2024), as this decline has been more pronounced in mathematics than in other subjects (Gottfried et al., 2001; Hedelin & Sjöberg, 1989).

As different instructional arrangements (i.e., teaching "situations") substantially impact students' state interest (Knogler et al., 2015), situational interest is a crucial factor in students’ learning (especially for unmotivated students; Hidi & Harackiewicz, 2000). Teachers cannot directly influence students' individual interests (i.e., students’ preexisting interests), but the situational interest generated by instruction-related factors is of paramount concern. That is, teachers can use the mechanisms responsible for the evolution of interest as it develops from situational into individual interest to promote interest or to compensate for the decline (i.e., hoping that a classroom rich in situational interest can alter an individual’s personal interest in the subject over time; Mitchell, 1993). In addition to factors related to a stimulating environment, such as stimulating tasks/problems (Harackiewicz et al., 2016; Rotgans & Schmidt, 2017a), or characteristics of the teachers, such as their enthusiasm (Jung et al., 2024), situational interest may be promoted if teaching is designed to avoid the characteristics that have been identified as responsible for declining interest. The decline in students' interest across secondary school can be attributed to different factors. For instance, in mathematics, studies have emphasized that students develop little interest because they have difficulties understanding complex concepts and are thereby overburdened or bored, as they perceive no utility or a lack of relevance in the content (Frenzel et al., 2010). Boredom is especially likely in mathematics, which has been documented for decades (Mitchell, 1993). Moreover, it is well known that students often struggle to maintain their initial enthusiasm and curiosity in mathematics (Frenzel et al., 2010), and mathematics appears to be particularly uninteresting to students, especially during puberty (Frenzel et al., 2010; Hoffmann et al., 1998).

Importantly, several recent studies that have used experience sampling methodology to study students' situational interest have shown that students' interest varies greatly from lesson to lesson. Most of the variance can be explained by intraindividual differences between students (Flunger et al., 2022; Järvinen et al., 2022; Martin et al., 2015; Parrisius et al., 2022; Patall et al., 2016). These findings have crucial implications: First, teachers are confronted with considerable heterogeneity in students' interests, and second, students' interests are sensitive to the nature of teaching during a lesson. The study by Flunger et al. (2022) showed that lesson-specific autonomy support by teachers was associated with students' engagement and motivation. Similarly, Heemskerk and Malmberg (2020) showed that student engagement was higher during individual tasks, teacher-supported tasks, and assessments than teacher-led instruction. Most relevant for the present study is Patall et al.’s (2018) finding that students became disengaged in lessons when the previous task was too difficult.

In summary, students are less likely to develop situational interest if they are bored (e.g., because the lesson is too easy) or overwhelmed (e.g., because the lesson is too complex) or if the teacher does not help students recognize why the learning material has utility for them (Ainley, 2006). Therefore, educators must balance the difficulty of instructional materials and activities to ensure they are appropriately challenging yet accessible. The extent to which a lesson can avoid underchallenging or overchallenging students and is therefore not perceived as either boring or overwhelming is crucial for arousing and maintaining students’ interest in teaching situations. Such teaching is referred to as adaptive teaching.

Technology-Enhanced, Adaptive Teaching as a Key Aspect for Promoting Students' Situational Interest

The idea of adapting teaching to students' differences to personalize students' learning experiences has a long history (Corno, 2008; Dockterman, 2018). It is one of the few teaching principles that has "withstood the dual tests of time and directed research" (Corno, 1995, p. 98). In their study on aptitude-treatment interaction (ATI), Cronbach and Snow (1977) showed that the effectiveness of instructional treatments depends on students' individual differences. More specifically, they found that lower ability students benefit more from teacher guidance than higher ability students do (Cronbach & Snow, 1977). This finding has been replicated numerous times and is now known as the expertise reversal effect (Kalyuga, 2007). The concept of adaptive teaching thus suggests that the level of guidance needs to be adapted to students' level of understanding so that each student can learn in their zone of proximal development (Corno, 2008; Vygotsky, 1979). Importantly, this concept applies to inter- and intraindividual differences: Low-achieving students need more structure and guidance from teachers than stronger students who are better prepared to self-regulate their learning processes. As students become more competent over time, the teacher must gradually decrease their guidance and hand over responsibility to the students to promote self-regulated learning. To adapt their teaching, teachers need to continuously assess students' levels of understanding.

The most visible manifestation of adaptive teaching can be seen in adaptations of various instructional features—such as teaching methods, materials, tasks, feedback, or explanations—to meet the specific needs of individual students or groups of students with similar needs. According to Corno and Snow (1986), adaptations can vary along a continuum from the macro-level to the micro-level. Whereas macro-adaptations refer to planned and longer term instructional adjustments for groups of students with similar competencies, micro-adaptations refer to spontaneous adjustments a teacher makes in response to individual students "on the fly" (Corno, 2008). Therefore, teachers must be flexible as they adapt their instruction to support various learners (Parsons et al., 2018).

It is a major challenge for teachers to implement adaptive teaching and get all students interested in the lessons, as not all students “have interests that are easily adaptable to school settings and academic learning” (Hidi & Harackiewicz, 2000, p. 157). Being flexible and becoming an “adaptive expert” (Darling-Hammond et al., 2005) is demanding for teachers, as adaptive teaching (in mathematics) requires teachers to reflect on students’ cognition, motivation, emotions, and learning trajectories and to provide feedback, modify curricular materials, and orchestrate classroom discourse (Gallagher et al., 2022; Schmid et al., 2022). Thus, adaptive teaching has often remained a theoretical ideal for decades, but its practical implementation has repeatedly failed (many teachers do not teach adaptively; Ankrum et al., 2020; Hardy et al., 2022; Pozas et al., 2020; Vaughn, 2019).

Against this backdrop, technology has the potential to support teachers in facilitating adaptive teaching (especially micro-adaptation; Aleven et al., 2017; Scheiter, 2017; Schmid et al., 2022; Xie et al., 2019), particularly in mathematics (Zhang et al., 2020). For instance, technology helps teachers scaffold students as the students solve problems (e.g., by using simulations or virtual reality to visually represent complex content; Kim & Hannafin, 2011; Puntambekar, 2022). This kind of scaffolding is especially suitable for supporting students’ interests (Harackiewicz & Knogler, 2017) or for performing formative assessments of students’ learning and providing feedback continuously by automating these processes (Mavroudi et al., 2018; Van Der Kleij & Adie, 2018). Moreover, technology can support adaptive collaboration between students through intelligent tutoring technology (Diziol et al., 2010). Thus, technology has the potential to help teachers provide adaptive instruction, for example, by avoiding simple tasks that lead to disengagement and boredom (Blayney et al., 2016). In support of this claim, in the International Computer and Information Literacy Study 2018, 87% of teachers agreed that technology helps students work at the level that is appropriate for their learning needs, and 91% said that technology helps students develop greater interest in learning (Fraillon et al., 2020).

Providing support for the potential benefits of technology for adaptive teaching comes from an empirical study by Walkington (2013) who investigated the effectiveness of using adaptive learning technologies to personalize mathematics (i.e., algebra) instruction to students' interests (145 ninth-graders) with an intelligent tutoring system in an experimental design. She found that the personalization had a particularly strong impact on students who were struggling with algebra. This finding suggests that technology-based adaptive teaching (i.e., adapted to students' interests) is particularly beneficial for students who struggle with mathematics, as grounding abstract mathematical concepts in contexts relevant to students' interests can make the concepts easier to understand and easier to learn.

Furthermore, technology has been found to have only small effects on students' achievement (Tamim et al., 2011; Zheng et al., 2016) and students’ motivation and attitudes (e.g., on students’ motivation to do mathematics: d = 0.30, Higgins et al., 2019; on students’ attitudes toward mathematics: g = .045, Hillmayr et al., 2020). The key to promoting student learning is not just that technology is used but how technology is implemented into teaching (Fütterer et al., 2022; Fütterer, Hoch, et al., 2023; Sailer et al., 2024), a finding that again applies in particular to adaptive learning systems (Keuning & Van Geel, 2021). In this vein, Sibley et al. (2024) showed that technology has great potential to support adaptive teaching, but its effectiveness depends on its thoughtful integration into teachers’ classroom instruction (i.e., the overuse of technology might overwhelm students). Moreover, Colliot et al. (2024) showed in an experimental study that it was not the use of tablet computers that improved students' performance but the personalized feedback that the intelligent tutoring system provided (see also the review by Maier & Klotz, 2022). Similar mechanisms can be assumed for students’ interests, where there is a need to distinguish between two different mechanisms behind the effect of technology on learning, particularly students' interests. The first effect is a direct effect of technology on students’ interest, known as the novelty effect, which is thought to be short-term but not sustainable. The novelty effect can be defined as "an improvement in learning when a new technology is introduced, attributable to increased interest in the new technology, that tends to diminish as students become more familiar with it" (Metcalf et al., 2019, p. 115). Indeed, computers have been found to trigger situational interest (Lepper & Malone, 1987) only in the short term. In a recent study, Jeno et al. (2019) found that whereas the novelty of educational technology attracts initial attention and interest, it does not sustain motivation or improve learning outcomes over time.

Such situations can be assigned to the first phase of interest genesis (triggered situational interest), as situational interest in this case is triggered by a sudden change in the learning environment (the introduction of [new] technology) and the immediate affective and emotional responses of the students (see, Hidi, 1990; Hidi & Harackiewicz, 2000; Hidi & Renninger, 2006; Hulleman et al., 2010).

However, as computers are usually not enough to maintain students’ interest in mathematics by themselves, the triggered interest must be stabilized through supportive conditions such as a change in instruction (maintained situational interest). If technology is used in the manner described above so that instructional methods change and the quality of teaching increases (i.e., it becomes more effective in generating learning), for example, by becoming more adaptive, then there is an indirect effect of technology on student learning, which is assumed to have a lasting effect (Clark, 1983, 1985). Such situations can be assigned to the second phase of interest genesis (maintained situational interest), where interest has the chance to stabilize. In their review, Zhang et al. (2020) found that technology-supported personalized learning was positively associated with students’ academic outcomes, attitudes toward learning, and engagement (see also Major et al., 2021).

Research Questions and Hypotheses

As situational interest has been shown to decline during secondary school, especially in mathematics, it is particularly important to find ways to promote and maintain students’ situational interest during this time. Adaptive teaching has proven to be an effective way to increase (situational) interest. Additionally, technology offers great potential for the implementation of adaptive teaching. Thus, in this study, we investigated direct effects of technology on students' interest in mathematics and indirect effects mediated by students’ perceptions of the extent to which digital instruction is adaptive by addressing the following research questions (RQ) and testing the associated hypotheses (H):

(RQ1) How does 1:1 technology (i.e., tablet computers for students) affect students' situational interest in mathematics (at the student and class levels)?

On the basis of previous research (e.g., Higgins et al., 2019; Tamim et al., 2011; Zheng et al., 2016) and due to potential corresponding novelty effects, we expected small positive effects (d ≤ 0.35) of 1:1 tablet computers on students' situational interest in mathematics at Level 1 (students; H1.1) and Level 2 (classes; H1.2).

(RQ2) How is student-perceived adaptive teaching in mathematics (at the student and class levels) associated with students' situational interest in mathematics (at the student and class levels)?

The more students perceive teaching in mathematics as adaptive, the higher students' situational interest in mathematics at Level 1 (students; H2.1) and Level 2 (classes; H2.2).

(RQ3) Does student-perceived adaptive teaching in mathematics (at the student and class levels) mediate the effect of 1:1 technology (i.e., tablet computers for students) on students' situational interest in mathematics (at the student and class levels)?

Student-perceived adaptive teaching in mathematics at least partially mediates the effect of 1:1 tablet computers on students' situational interest in mathematics at Level 1 (students; H3.1) and Level 2 (classes; H3.2). For all hypotheses referring to effects at Level 1 and Level 2, we expected the same direction but weaker effects at the class level (Level 2) than at the student level (Level 1).

In reporting our research, we adhered to the CONSORT 2010 statement for randomized controlled trials (Cuschieri, 2019; the checklist is available in our OSF project).

Context of the Study

Data stemmed from the larger research project tabletBW meets science, which is connected to the tabletBW school trial, initiated by the federal state of Baden-Württemberg, Germany, to promote technology-enhanced teaching and learning. Secondary schools (only Gymnasiums, i.e., the highest track in German high schools) from Baden-Württemberg could apply to participate in the school trial. From the pool of 28 schools that applied, a researcher who was not part of the project team randomly allocated 14 schools to the intervention condition and 14 to the control condition utilizing random numbers in a statistics program. For both conditions, stratified sampling was used to sample from the four school districts in the federal state to the same extent. Whereas the intervention schools received funding to equip students and teachers in four classes with tablet computers on a one-to-one basis, the control schools did not receive funding from the research project. In each school, students from four Grade 7 classes (i.e., two classes from 2018 = first cohort, two classes from 2019 = second cohort) were asked to participate in the research project. Data were collected from teachers, students, and their parents or legal guardians over the course of 3 years. For the present analyses, we use data from students who provided data at the baseline measurement (t₀; in spring 2018 and 2019 in Cohorts 1 and 2, respectively) and the measurement 4 months later (t₁; in summer 2018 and 2019 in Cohorts 1 and 2, respectively) from both cohorts. If a student did not participate in either of these two measurement points, we did not include this student in our analyses. For further details, see also Fütterer et al. (2022), Fütterer, Hoch, et al. (2023), and Hoch et al. (2024).

The procedures followed APA guidelines (e.g., voluntary participation and informed consent). Furthermore, the Ministry of Culture, Youth, and Sports Baden-Württemberg granted institutional approval for the data protection procedure, content, materials, and study design.

Sample

We used data from both cohorts from the research project tabletBW meets science. There were three levels of data. We used data from 2,601 students nested in 110 classes (56 intervention and 54 control classes; four classes within each school)2 that were nested in 28 schools (14 intervention and 14 control schools; all academic track schools). The data consisted of 1,220 students in control schools and 1,381 in intervention schools. At the first measurement point (t₀), the mean age of the students was M = 12.52 years (SD = 0.60), and 50% of them identified as female (for an overview of a detailed sample description, see Table 1).

Table 1

Sample Description
	Sample size	Age in years			Female
	N	n	M	SD	n	%
Overall	2,601	2,517	12.52	0.60	2,519	50
Control condition	1,220 (47%)	1,186	12.55	0.61	1,184	49
Intervention condition	1,381 (53%)	1,331	12.49	0.59	1,335	51
Cohort 1	1,400 (54%)	1,365	12.51	0.59	1,365	49
Cohort 2	1,201 (46%)	1,152	12.54	0.61	1,154	51
Note. The statistics are the baseline demographic.

We followed an intention-to-treat (ITT) approach (McCoy, 2017). That is, we considered all randomized students in the analyses, whether their data were missing at the second measurement point t₁ or not. This approach was justified because, when values at the first measurement point were present, the absence of values at the second measurement point hardly affected the estimation of missing values (Rabe-Hesketh & Skrondal, 2023).

Measures

See Appendix A for an overview of all items used in this study. For an overview of detailed scale statistics, see Table 2.

Table 2

Scale Means, Standard Deviations, and Ranges
	N	M	SD	Range	α
Overall
Situational interest t₀	2,193	2.60	0.91	1–4	.95
Situational interest t₁	2,037	2.65	0.97	1–4	.97
Adaptive teaching t₀	1,070	2.82	0.76	1–4	.89
Adaptive teaching t₁	2,124	2.73	0.88	1–4	.94
Intervention condition
Situational interest t₀	1,333	1.72	0.89	1–4	.95
Situational interest t₁	1,035	2.89	0.90	1–4	.97
Adaptive teaching t₀	602	2.90	0.73	1–4	.88
Adaptive teaching t₁	1,078	2.89	0.88	1–4	.95
Control condition
Situational interest t₀	1,060	2.47	0.92	1–4	.94
Situational interest t₁	1,002	2.40	0.98	1–4	.97
Adaptive teaching t₀	468	2.90	0.78	1–4	.89
Adaptive teaching t₁	1,046	2.57	0.85	1–4	.93
Note. t₀ = First measurement point, t₁ = Second measurement point.

Students' Perceived Situational Interest

We operationalized the dependent variable students' perceived situational interest in mathematics—a global average measure of the teaching situation across 4 months—by computing the arithmetic mean of a five-item scale (adapted from Knogler et al., 2015; e.g., “In these lessons, the teaching aroused my curiosity”). The students rated the items on a 4-point scale ranging from 1 (not true at all) to 4 (exactly true) at the first and second measurement points. At the second measurement point, students in the intervention condition were asked to rate the items for lessons in which the tablet computers were used. As interest is directly linked to individuals and their perceptions of situations, we used the state-of-the-art approach of using student self-reports to assess students' situational interests (see Ainley, 2006). In accordance with Taber’s (2018) guidelines, the internal consistency of the scale when collapsing across the two conditions could be classified as high (t₀: Cronbach's alpha [α] = .95; t₁: α = .97).

Condition

We used the dichotomous variable condition (0 = control condition, 1 = intervention condition) as the independent variable. Students were assigned to the intervention condition if they attended a school that had been selected to be equipped with tablets and to the control condition if they attended a school that had not been equipped with tablets.

Student-Perceived Adaptive Teaching

We operationalized student-perceived adaptive teaching in mathematics (which functioned as an independent variable and a mediator) by computing the arithmetic mean of a five-item scale (adapted from Bürgermeister et al., 2011; e.g., “Our teacher looks at where I have problems learning”). At the second measurement point, the students rated the items on a 4-point scale ranging from 1 (not true at all) to 4 (exactly true) regarding their perceptions of the teaching during the 4 months between the first and second measurement points. We used student ratings because students can provide differentiated assessments of various aspects of teaching quality (Wagner et al., 2013), and such assessments can be valid measures for evaluating various aspects of teaching quality (Göllner et al., 2018; Wagner et al., 2016), such as adaptive teaching. In accordance with Taber’s (2018) guidelines, the internal consistency of the scale when collapsing across the two conditions could be classified as high (t₀: α = .89; t₁: α = .94).

Covariates

We controlled for important prerequisites of the students at Level 1 and aggregated class characteristics at Level 2. We selected variables in accordance with the What Works Clearinghouse (WWC) standards for equivalence (WWC, 2022). We compared baseline differences measured in standardized effect size (ES) units (i.e., Hedge's g) in both cohorts against the WWC standard for baseline equivalence. That is, 0.00 ≤ |Baseline ES| ≤ 0.05 satisfied baseline equivalence, 0.00 < |Baseline ES| ≤ 0.25 required statistical adjustments to satisfy baseline equivalence, and |Baseline ES| > 0.25 did not satisfy baseline equivalence. We analyzed students' self-reported gender (0 = male, 1 = female), students' self-reported migration background (0 = no parent was born abroad, 1 = at least one parent was born abroad), students' self-reported age (measured continuously in years), students' self-reported socioeconomic status (measured continuously as number of books at home), students' general cognitive ability measured with a subtest on figure analogies from the KFT 4–12 + R (Heller & Perleth, 2000), students' convention and rule knowledge measured with the mathematics test KRW (Ennemoser et al., 2011), students' self-reported grades in mathematics (1 = very good to 6 = insufficient), and students' self-reported mathematics self-concept measured as the arithmetic mean of four items (adapted from Gaspard et al., 2021; e.g., “I’m not particularly good at math lessons”) rated on a 4-point scale ranging from 1 (not true at all) to 4 (exactly true). In accordance with to Taber’s (2018) guidelines, the internal consistency of the scale when collapsing across the two conditions at the first measurement point t₀ could be classified as high (α = .90).

We found baseline differences between the control and the intervention conditions for students' age (d = 0.10), socioeconomic status (d = 0.16), KRW test score (d = 0.28), and grade (d = 0.08); thus, we included these covariates in the analyses at Level 1 and aggregated them at Level 2. In addition, we controlled for students' cohort membership (0 = Cohort 1, 1 = Cohort 2) and situational interest at the first measurement point t₀ (d = 0.27).

Procedure

Data were collected in classrooms by trained research assistants at each measurement point. After students and their legal guardians had given their consent to participate, the students completed the questionnaires online on tablet computers (or on paper if digital completion was not possible). The questionnaires comprised self-reports on students' attitudes (e.g., situational interest), students' perceptions of lessons with and without tablet computers (e.g., student-perceived adaptive teaching), and students' backgrounds (e.g., demographics). Students' abilities were tested with paper-pencil instruments (e.g., general cognitive ability). Relatively stable variables were assessed only once at t₀ (e.g., personality). Variables that were expected to show greater change over time were assessed at each measurement point (e.g., students' motivation to use technology). Data collection at t₀ was divided into two sessions of 90 min, thus lasting 180 min in total. Data collection at the subsequent measurement point lasted 90 min.

Statistical Analyses

Data preparation and analyses (e.g., scale reliabilities and descriptive statistics) were performed in R 4.3.2 (R Core Team, 2023). We used the R package MplusAutomation 1.1.1 (Hallquist & Wiley, 2018) and Mplus 8.6 (Muthén & Muthén, 1998–2017) to test multilevel (i.e., students [Level 1] nested in classes [Level 2] nested in schools [Level 3]) random intercept models (Hox et al., 2017). All variables were manifest, and we obtained the observed group average of predictors at the classroom level (Level 2) by aggregating variables at the student level (Level 1). We utilized this approach rather than, for example, a multilevel latent covariate model (Lüdtke et al., 2008) because the variables at the student level were shown to be reliable measures of the unobserved group average (ICC(2) = .77 for adaptive teaching).

To investigate the effects of 1:1 tablet computers (at the school level) and students' situational interest in mathematics (at the student and class levels; RQ1), we tested a three-level random intercept model in which the condition variable was the predictor at Level 3 (see Equation A in the Appendix). More specifically, to analyze the contextual effect, we centered the Level 1 and Level 2 predictors at the grand mean (Enders & Tofighi, 2007; Hamaker & Muthén, 2020; Lüdtke et al., 2009).

We also tested a random-intercept model to investigate the associations between student-perceived adaptive teaching in mathematics (at the student and class levels) and students' situational interest in mathematics (at the student and class levels; RQ2). We added student-perceived adaptive teaching as a predictor on Levels 1 and 2. To be able to disentangle within-class and between-class as well as within-school and between-school effects of students' perceptions of adaptive teaching on students' situational interest, we group-mean-centered (i.e., school-mean-centered) class-mean aggregates as predictors on the between-class level, and we group-mean-centered (i.e., class-mean-centered) the predictors on the individual student level (Hamaker & Muthén, 2020; Lüdtke et al., 2009).

To investigate whether student-perceived adaptive teaching mediated the effect of 1:1 tablet computers on students' situational interest in mathematics (at the student and class levels; RQ3), we used the model that was applied to RQ1 but added the grand-mean-centered student-perceived adaptive teaching variables as predictors on Levels 1 and 2 and regressed them on the condition variable (located at Level 3). Using the model constraint option as implemented in Mplus, we computed the indirect effects at Levels 1 and 2 of equipping students with 1:1 tablet computers on students' situational interest via student-perceived adaptive teaching in two separate models.

The schematic representations in Figs. 1a to 1c provide an overview of the models.

We treated missing values with full information maximum likelihood (FIML) estimation (Graham, 2012; Lüdtke et al., 2007; van Buuren, 2018) as implemented in Mplus because FIML provides less biased estimation and greater robustness when assumptions are violated (e.g., missing at random) than traditional approaches do (e.g., listwise deletion).

We applied the Benjamini-Hochberg (1995) correction for multiple testing. To counteract the accumulation of alpha errors, we corrected the estimated p values for each model.

The data we used as well as the complete reproducible code, and the scale book can be accessed at our Open Science Framework (OSF) project: https://osf.io/m6zyw/?view_only=b542c8dbe39640c18e867c7b723d01f0

[1] One school did not participate in the second cohort; thus, it contributed only two classes to the sample.

Descriptive Statistics and Correlations

The descriptive statistics, intraclass correlations (ICCs), bivariate correlations, and sample sizes for the scales used in this study are presented in Tables 2, 3, and 4. The ICCs indicated that 11–12% of the variance in student-rated situational interest and adaptive teaching at t₁ could be attributed to between-class effects (Level 2) and 8–9% to between- school effects (Level 3). We found moderate correlations between student-rated situational interest and adaptive teaching within and between the two measurement points at the student level (Level 1; Table 3). The correlations between the control and intervention conditions were comparable (Table 4). By contrast, we found strong correlations (Table 3) at the class level (Level 2).

Table 3

Product-Moment Correlations, Descriptive Statistics, and Intraclass Correlations of the Scales
	(1)	(2)	(3)	(4)
Situational interest t₀ (1)		.96^*	.97^*	.95^*
Situational interest t₁ (2)	.49^*		.94^*	.99^*
Adaptive teaching t₀ (3)	.54^*	.34^*		.93^*
Adaptive teaching t₁ (4)	.36^*	.56^*	.41^*
N_Level 1	2,193	2,037	1,070	2,124
M	2.60	2.65	2.82	2.73
Var	0.83	0.94	0.58	0.78
ICC	.14	.11	.17	.11
Note. Level 1 correlations are presented below the diagonal; Level 2 correlations are presented above the diagonal. M = Arithmetic mean, Var = Variance, ICC = Intraclass correlation. t₀ = First measurement point, t₁ = Second measurement point. The response scales ranged from 1 to 4. *p < .001.

Table 4

Pearson Product-Moment Correlation Coefficients at the Student Level for the Control and Intervention Conditions
	(1)	(2)	(3)	(4)
Situational interest t₀ (1)		.48	.62	.42
Situational interest t₁ (2)	.63		.39	.57
Adaptive teaching t₀ (3)	.62	.43		.50
Adaptive teaching t₁ (4)	.48	.67	.53
Note. n = 390 to 1,020, pairwise deletion. Level 1 correlations in the control condition are presented below the diagonal (i.e., lower triangle), Level 2 correlations in the intervention condition are presented above the diagonal (i.e., upper triangle). t₀ = First measurement point, t₁ = Second measurement point. All correlation coefficients were statistically significant at α = .05.

1:1 Technology and Students' Situational Interest

Regarding the effect of equipping students with 1:1 tablet computers on students' situational interest (RQ1), we found a statistically significant, positive effect, b (SE) = 0.25 (0.06), b^* = .80, p < .001, when controlling for the Level 1 and Level 2 variables. This context effect means that, when holding Level 1 covariates (e.g., age, KRW test score) and Level 2 covariates (e.g., KRW test score aggregated at the class level) constant, students who were in the intervention condition showed 0.25 points higher situational interest than students in the control condition 4 months after begin equipped with tablet computers (see Raudenbush & Bryk, 2010). Table 5 presents an overview of all the statistics.

Table 5

Fixed and Random Effects in the Random Intercept Model for Student-Reported Situational Interest Predicted by 1:1 Tablet Computers
					Situational interest
					b		SE			p		b^*
Intercept (Mean)					2.44		0.05			< .001
Level 1
Covariates
Situational interest^a					0.49		0.03			< .001		.51
Students' age^a					-0.01		0.03			.769		− .01
Students’ SES^a					-0.00		0.02			.809		− .01
Students' KRW score^a					0.01		0.00			< .001		.10
Students' grade^a					-0.01		0.02			.597		− .02
Level 2
Covariates
Situational interest^a					0.35		0.07			< .001		.76
Students' age^a					0.17		0.15			.270		.13
Students’ SES^a					0.04		0.06			.554		.09
Students' KRW score^a					-0.02		0.01			.040^†		− .26
Students' grade^a					-0.09		0.05			.061		− .35
Cohort					0.14		0.07			.062		.34
Level 3
Condition					0.25		0.06			< .001		.80
					Var							R²
Level 1 (Residual)					0.57							.28
Level 2 (Residual)					0.12							.71
Level 3 (Intercept)					0.01							.64
Note. SE = Standard error; b^* = standardized slope; SES = socioeconomic status; KRW = Test of convention and rule knowledge using mathematics. ^a Assessed at the first measurement point t₀;^✝ No longer statistically significant after the Benjamini-Hochberg (1995) correction was applied.
Table 6 Fixed and Random Effects in the Random Intercept Model of Student-Reported Situational Interest Predicted by Student Perceived Adaptive Teaching
					Situational interest
					b		SE			p		b^*
Intercept (Mean)					2.63		0.07			< .001
Level 1
Adaptive teaching					0.49		0.03			< .001		.43
Covariates
Situational interest^a					0.34		0.03			< .001		.32
Students' age^a					-0.03		0.02			.249		− .02
Students’ SES^a					-0.01		0.01			.379		− .02
Students' KRW score^a					0.01		0.00			< .001		.08
Students' grade^a					-0.01		0.02			.731		− .01
Level 2
Adaptive teaching					0.66		0.11			< .001		.68
Covariates
Situational interest^a					0.27		0.10			.005		.36
Students' age^a					0.03		0.11			.783		.01
Students’ SES^a					0.04		0.05			.441		.05
Students' KRW score^a					-0.01		0.01			.157		− .10
Students' grade^a					-0.09		0.06			.131		− .22
Cohort					0.15		0.07			.038^†		.12
					Var							R²
Level 1 (Residual)					0.45							.41
Level 2 (Residual)					0.01							.93
Level 3 (Mean)					0.10
Note. SE = Standard error, b^* = standardized slope. SES = socioeconomic status, KRW = Test of convention and rule knowledge using mathematics. ^a Assessed at the first measurement point t₀; ^✝ No longer statistically significant after the Benjamini-Hochberg (1995) correction was applied.
Table 7 Fixed and Random Effects in the Random Intercept Model of Student-Reported Situational Interest Predicted by 1:1 Tablet Computers Mediated by Student-Perceived Adaptive Teaching
Level	Effect of condition on mediator (a)			Effect of mediator on situational interest (b)					Indirect effect (ab)
	b	SE	p	b		SE		p	b		SE		p
1	0.25	0.07	.001	0.41		0.03		< .001	0.12		0.04		.001
2	0.14	0.07	.031^†	0.57		0.09		< .001	0.08		0.04		.025^†
Note. Two models for each indirect effect at each level were run. SE = Standard error. ^✝ No longer statistically significant after the Benjamini-Hochberg (1995) correction was applied.

Associations Between Adaptive Teaching and Students' Situational Interest

Regarding the associations between student-perceived adaptive teaching and students' situational interest (RQ2), the random intercept model showed statistically significant positive associations between students' perceived adaptive teaching and their interest in mathematics on the student (Level 1) and classroom levels (Level 2; Table 6). That is, classrooms with higher adaptive teaching were also the classrooms where students reported higher situational interest, b (SE) = 0.49 (0.03), b^* = .43, p < .001, and students who reported higher adaptive teaching also reported higher situational interest than students reporting lower levels of adaptive teaching, b (SE) = 0.66 (0.11), b^* = .68, p < .001. Table 6 presents an overview of all statistics.

Mediation of the Effect of 1:1 Technology on Students' Situational Interest via Adaptive Teaching Interest

Regarding the mediation of the effect of 1:1 tablet computers on students' situational interest via student-perceived adaptive teaching (RQ3), we found a statistically significant, positive indirect effect on the student level, b (SE) = 0.12 (0.04), p = .001. After applying the Benjamini-Hochberg (1995) correction, b (SE) = 0.08 (0.04), p = .025, the effect was no longer statistically significant at the classroom level. That is, students who were equipped with 1:1 technology perceived the teaching as more adaptive, which had a positive effect on students' situational interest. Table 7 provides an overview of all statistics.

The study presented here was part of a randomized controlled school trial in which students were either equipped or not equipped with tablets, and the effect of this technology on students’ interest was investigated in a longitudinal study. Moreover, we examined the relationships between equipping students with 1:1 tablet computers, student-perceived adaptive teaching, and students’ situational interest. In line with our expectations, we found that tablet computers affect students' situational interests and perceived adaptive teaching were positively related to students' situational interests. Furthermore, we found evidence that student-perceived adaptive teaching—on the student level—mediated the relationship between tablet computers and students' situational interest.

H1.1 and H1.2 focused on the effect of 1:1 tablet computers on students' situational interest in mathemtaics. In line with our expectations, positive relationships were observed at the student (Level 1) and class levels (Level 2). The effects were larger than we expected; however, most research on the influence of technological equipment has focused on achievement measures as outcomes, whereas far less is known about effects on motivational variables. For two reasons, one might assume that our study's effects on motivational outcomes would be larger than on achievement or performance measures. First, our observation period referred to the first 4 months of the tablet roll-out. Thus, although novelty effects are typically shorter (Clark, 1985), it is possible that a novelty effect occurred, thus intensifying the effect on students’ interest. Second, the effect might also have been fueled by the fact that students in the control schools were less motivated because they did not receive the equipment they had hoped for. However, such frustration should already have been evident at t₀, particularly in general motivation for tablet use, and presumably less in situational interest in a particular subject.

The results supported our expectations for H2.1 and H2.2 by showing a positive association between student-perceived adaptive teaching in mathematics lessons and students' situational interest in mathematics. This finding is aligned with the theoretical assumption that adapting one’s teaching to students' individual needs fosters their situational interest. However, contrary to our expectations, the associations were stronger at the class level than at the individual level. This finding could reflect that adaptations were made to longer term teaching sequences for groups of students (i.e., macro-adaptations) rather than spontaneous situation-specific adaptations for individual students (i.e., micro-adaptions). However, more data are needed to verify that this explanation holds, especially at the process level. Observational studies or applications of experience sampling are therefore needed to observe and evaluate the adaptations and related effects with greater precision.

Furthermore, we showed that student-perceived adaptive teaching in mathematics mediated the effect of equipping students with tablet computers on students' situational interest in mathematics at the student level (H3.1). This finding underscores the idea that technology alone is not sufficient for improving teaching or learning but has specific potential that might be useful for fostering teaching or learning processes. Although the same pattern emerged at the classroom level, the indirect effect was not statistically significant. This finding is in line with our expectations (stronger effects on the student level than on the classroom level).

Strengths, Limitations, and Future Research

Our study was a randomized controlled trial in which the relationships between technology, adaptive teaching, and situational interest were investigated in a real classroom context. Thus, the research was conducted in an ecologically valid setting, thereby strengthening the validity and practical relevance of our results.

Whereas many previous studies have addressed adaptive teaching with a qualitative approach (e.g., see the research synthesis by Parsons et al., 2018), in this study, we used a quantitative, longitudinal approach with a large sample to show the positive relationships between technology, adaptive teaching, and students' situational interests. With this approach, this study provided initial evidence that teaching is moving toward more adaptive teaching through technology.

Whereas the approach was promising and can be considered a strength of the study, the pitfall of the large sample size is that only student ratings were considered, and detailed data on teaching processes is missing. Therefore, future studies should focus on how technology is actually used in classroom teaching through other methodological approaches, such as observations of lessons, which could be supplemented by experience sampling. Experience sampling would allow the degree of adaptivity to be measured per lesson (it can be assumed that this adaptivity varies greatly from lesson to lesson as well; Flunger et al., 2022) and the associated situational interest to be recorded on a genuinely situational basis. Such fine-grained data could help better explain the mechanisms that underlie the relationships between technology, adaptive teaching, and student achievement or motivation outcomes.

Another shortcoming of our study is related to how students’ perceived adaptive teaching was assessed. Although studies have shown that student self-reports are valid for assessing aspects of teaching quality (Göllner et al., 2018; Wagner et al., 2016), they also come with some flaws, as students are not only evaluators of lessons but also heavily involved actors who do not have professional (i.e., didactic or pedagogical) expertise (Göllner et al., 2016). Moreover, whether our measure considers whether students are learning in their zone of proximal development remains an open issue. Therefore, developing more precise measures of adaptive teaching remains an endeavor for future research.

Finally, it is important to exercise caution when generalizing these results, as the observed effects may be context-specific and influenced by various factors including the novelty of the technology, the implementation of adaptive strategies, and the classroom environment. Future research should focus on replicating these findings in diverse educational settings to assess the broader applicability of the interventions.

Implications for Research and Practice

As outlined before, more research is needed to better understand how teachers adapt their teaching to meet students’ needs. Specifically, more research is needed to understand the relationships between the different aspects of adaptive teaching. For instance, the field needs large-scale, longitudinal research that explicitly ties teachers’ instructional adaptations to student outcomes to provide insights into the specific nature of adaptations that positively influence students (Parsons et al., 2018).

An implication for practice can be deduced from the effect of technology on students' situational interests via adaptive teaching. Thus, technology might be a means to (more easily) implement adaptive teaching and to pique students' interest in a specific domain (e.g., mathematics in our study). It can be the first step toward developing and sustaining a stable level of interest. Effective ways to promote students’ interest are particularly relevant during adolescence, as a decline in interest and engagement can be observed in adolescence (Eccles & Midgley, 1989; Kunter et al., 2007; Wigfield et al., 2006). Because of rapid technological developments, especially in artificial-intelligence-based systems, it can be assumed (and has already been observed) that adaptive teaching will become easier for teachers to implement (Asrifan & Dewi, 2024; Fütterer, Fischer, et al., 2023; Gligorea et al., 2023; Hou et al., 2024; Imhof et al., 2020; Kasneci et al., 2023; Xu et al., 2024).

Furthermore, given that teachers are crucial for effective technology-based adaptive teaching (Keuning & Van Geel, 2021), the results also emphasize that it is worth investing in teacher professional development to train teachers how to use technology for adaptive teaching (Fütterer et al., 2024; Fütterer, Scherer, et al., 2023), especially to help them become aware of important boundary conditions, such as the overuse of technology that could harm teaching effectiveness (Sibley et al., 2024).

In conclusion, our study highlights the potential that technology holds for implementing adaptive teaching to thereby promote students’ motivation. The evidence of a mediating effect of students’ perceptions of adaptive teaching emphasizes the idea that technological devices themselves do not appear to be sufficient. Rather, technology appears to have the potential to facilitate adaptive teaching. To exploit this potential, teachers need to design classroom activities in such a way that they use the technology reasonably, for example, to adaptively respond to the needs of students, which might then have a positive impact on students.

Acknowledgments

This paper used data from the research project tabletBW meets science, which is connected to the tabletBW school trial. The school trial was initiated by the Ministry of Culture, Youth, and Sports (KM). The Ministry’s data protection officer granted approval for the research project.

This research was supported by the Postdoctoral Academy of Education Sciences and Psychology of the Hector Research Institute of Education Sciences and Psychology, Tübingen, funded by the Baden-Württemberg Ministry of Science, Research, and the Arts.

We have no known conflicts of interest to disclose. This study was preregistered at PsychArchives (https://doi.org/10.23668/psycharchives.14090). Data, complete reproducible code, and supplementary material can be accessed at https://osf.io/m6zyw/?view_only=b542c8dbe39640c18e867c7b723d01f0. The authors are responsible for the contents of this publication.

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Uncovering the Relationship Between Technology-Enhanced, Adaptive Teaching and Situational Interest in Mathematics in a Randomized Controlled Trial

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Abstract

Introduction

Theoretical Background

Students’ Situational Interest

Technology-Enhanced, Adaptive Teaching as a Key Aspect for Promoting Students' Situational Interest

Research Questions and Hypotheses

Method

Context of the Study

Sample

Measures

Students' Perceived Situational Interest

Condition

Student-Perceived Adaptive Teaching

Covariates

Procedure

Statistical Analyses

Results

Descriptive Statistics and Correlations

1:1 Technology and Students' Situational Interest

Associations Between Adaptive Teaching and Students' Situational Interest

Mediation of the Effect of 1:1 Technology on Students' Situational Interest via Adaptive Teaching Interest

Discussion

Strengths, Limitations, and Future Research

Implications for Research and Practice

Conclusion

Declarations

Acknowledgments

References

Additional Declarations

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