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Pre-print. Please see the published book chapter, or contact the author before citing:

Moeller, J., Spicer, J., Salmela-Aro, K., & Schneider, B. (2017). Advances in the research on

situation-specific and contextual aspects of student engagement. In: I. Schoon & R. K. Silbereisen

(Eds.). Pathways to Adulthood. Educational opportunities, motivation and attainment in times of

social change (pp.119-136). London: UCL IOE Press.

Advances in the research on situation-specific and contextual aspects of student engagement

Julia Moeller, Justina Spicer, Katariina Salmela-Aro and Barbara Schneider

Abstract

Research on student engagement often uses diverse definitions and measures, which can

lead to confusion in the understanding and application of findings. As previous reviews of the

literature have shown, these measures can overlap with other constructs, such as motivation, but

also can differ in other regards. This chapter responds to the research reviews that call for a more

fine-tuned approach in capturing and integrating the measurement of engagement. We first

provide an overview of the research organized into three components of engagement: (1) person-

specific, (2) situation-specific, and (3) contextual determinants. Then we discuss how these

different aspects of engagement can be investigated. We conclude by describing methods for

measuring engagement in situation- and context-specific settings as well as offer future directions

for research.

Keywords: engagement, situational interest, motivation, learning context, person-oriented approaches, Flow, demands-resource model, experience sampling method, repeated measures, profiles

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Interdisciplinary perspectives of engagement

Student engagement is a key concept in understanding optimal learning motivation and

behavior, and a critical component in addressing other mechanisms such as increasing

academic motivation and preventing school dropout. The research on engagement is

interdisciplinary and has been summarized in several review articles and handbooks (e.g.,

Eccles and Wang, 2012; Fredricks, Blumenfeld, and Paris, 2004; Jimerson, Campos, and

Greif, 2003; Lawson and Lawson, 2013; Christenson and Reschly, 2012). These reviews have

found that the term ‘student engagement’ is used as a broad and multifaceted construct.

Student engagement is influenced by characteristics of the context (e.g., societal gender

stereotypes), person (e.g., academic self-concept) and situation (e.g., being presented with a

challenge). The research on engagement is partly motivated by the hope that studying the

malleable aspects of student motivation can enhance our understanding of how students feel,

think, and act towards learning tasks and school in general, with the goal to support the

students’ learning processes. However, the malleable, situation-specific aspects of

engagement are often mentioned but understudied and not well integrated in models and

measures of student engagement. The present chapter addresses these issues. First, we give an

overview about the research on person-specific, situation-specific, and contextual

determinants of student engagement, then we show how these engagement determinants can

be investigated, and finally we describe methods for measuring situation- and context-specific

aspects of student engagement.

General definition of engagement

Student engagement is a term that summarizes students’ characteristics that are

beneficial for their learning, achievement, adjustment in school, and educational pathways.

Engagement is thus a socially desired outcome. Student engagement is not a theoretically

bound, clear-cut entity (Eccles and Wang, 2012; Reschly and Christenson, 2012), but rather a

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multifaceted construct for which many different definitions, components, and measures have

been suggested. Most authors agree that student engagement includes cognitive, emotional,

and behavioral components (Fredricks et al., 2004). This, however, is a rough distinction since

these components are often entangled and interdependent (see Damásio, 1994).

Student engagement is considered a malleable and partly situation-dependent form of

learning motivation and behavior, something that, if enhanced, is supposed to ameliorate

academic outcomes (Fredricks et al., 2004; Lawson and Lawson, 2013). Aspects of

engagement can also be distinguished by the specific content they refer to (e.g., test

engagement, classroom engagement, and extracurricular engagement, see Lau and Roeser,

2002), or by their sources of variation and determinants such as contextual, personal and

situational engagement (Fredricks et al., 2004). Here it is argued, that in order to understand

why individuals engage in or disengage from specific tasks, and how such short-term

engagement eventually crystalizes to stable behavioral patterns, we must study in-the-moment

components of engagement.

How is engagement different/similar compared to other multifaceted constructs of

motivation?

Researchers, educators, and educational policy makers have made engagement an

omnipresent term. Unfortunately, this has created much confusion about its meaning. Despite

several reviews systematizing the definitions and measures of engagement, Reschly and

Christenson (2012) point out that there is still confusion due to terminological ambiguities

such as labeling different phenomena with the same term (‘engagement’) and using different

labels to describe the same phenomenon. To remedy such terminological ambiguities, we

discuss some overlaps between engagement and other multifaceted constructs of motivation.

Passion, similar to engagement, is a multifaceted construct of socially desired learning

motivation (for a review see Moeller, 2014). According to an often-cited definition, passion

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describes the inclination of a person towards an activity that a person likes, finds important,

invests time and energy in, and with which the individual identifies (Vallerand et al., 2003).

Passion and engagement overlap in the components of commitment and investment of

resources/effort (see Fredricks et al., 2004), flow (see Shernoff, Csikszentmihalyi, Schneider,

and Shernoff, 2003), and energy (see Vallerand et al., 2003 and Salmela-Aro and Upadaya,

2012). One difference between school engagement and passion is that passion typically refers

to one specific topic or type of activities (e.g., playing soccer and doing activities related to

playing soccer), whereas school engagement can refer to a large variety of topics and

activities, if they are learning-related. The literature on passion and engagement furthermore

differ in their emphasis on subgroups of beneficial versus harmful forms of motivation. The

passion research mainly focuses on differences between adaptive, beneficial forms of passion

(‘harmonious passion’) and maladaptive, harmful forms (‘obsessive passion’), while the

engagement literature mostly focuses on the positive forms and outcomes of the construct.

However, both student engagement and harmonious passion co-occur with negative

experiences in many individuals, as recent studies have shown (Moeller, Keiner, and

Grassinger, 2015; Salmela-Aro, Moeller, Schneider, Spicer, and Lavonen, 2016; Salmela-Aro

and Upadyaya, 2014; Tuominen-Soini and Salmela-Aro, 2014; Wang and Peck, 2013).

Intrinsic motivation is another multifaceted psychological construct that describes

reasons why individuals voluntarily engage in activities without external constraints or

rewards (Ryan and Deci, 2000). Intrinsic motivation comprises different facets, such as

interest, flow and positive emotions, among others, and thus overlaps strongly with

engagement. A difference between both constructs is that school engagement also includes

aspects beyond intrinsic motivation (cognitive concentration and investment, behavior in

accordance to school norms disregarding of the motives, persistence in frustrating, effortful

learning activities), which is known to be important in the development of excellent skills

(Ericsson, Krampe, and Tesch-Römer, 1993, Ericsson and Charness, 1994).

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Research gaps regarding state aspects, stable aspects and contextual aspects

While many studies use the term engagement broadly, there is a lack of studies on the

more specific aspects of engagement, particularly situation- and domain-specific measures

(Fredricks and McColskey, 2012). Also lacking are theoretical models to specify the distinct

contribution of specific components of engagement to relevant outcomes and models of the

mechanisms through which situation-, person- and context-specific aspects of engagement

influence the addressed outcomes. To compare the influence of different contexts on student

engagement, it is desirable to have a conception of engagement that can be applied across

different contexts and yet captures context-specific characteristics.

The research on student engagement largely addresses correlations between

engagement and other variables (e.g., dropping out), but as Fredricks and McColskey (2012)

argue, this focus might ignore differently functioning subgroups. The few approaches that

have analyzed subgroups have contributed to our understanding of engagement dramatically.

For example, despite the negative correlation between student engagement and burnout

(Salmela-Aro and Upadyaya, 2014), recent studies found that up to one out of four Finnish

high school students were simultaneously highly engaged and exhausted (Salmela-Aro et al.,

2016; Tuominen-Soini and Salmela-Aro 2014). These students were disproportionally likely

to become disengaged at later time points. These findings imply that highly motivated

students are not necessarily those the teacher does not need to worry about, but that on the

contrary highly engaged students might need specific support to acquire and maintain their

resources and motivation. This and further studies have shown how important it is to consider

‘the dark side’ of high engagement in combined models of study demands and resources (see

also Salmela-Aro et al., 2016; Salmela-Aro and Upadyaya, 2014).

Investigating engagement

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Comprehensive investigation of engagement includes several determinants, such as

person-specific factors that contribute to students’ engagement; the specific situation students

experience; and the context of the task or activity. This section discusses these three aspects of

the learning environment.

Inter-individual variation: stable person-specific aspects of student engagement

Some students are generally more intensively, and/or more often engaged in school-

related tasks than other students, despite fluctuations between specific situations or

experiences (Tuominen-Soini and Salmela-Aro, 2014). That is, the level of a student’s

engagement remains partially stable across situations/contexts, which is labeled the ‘person-

specific’ component (Salmela-Aro and Upadyaya, 2014). Examples of these stable

components include identification with school norms (Finn, 1989; Voelkl, 1997) and goal-

orientation (e.g., the pursuit of education-related long-term goals). Furthermore, individuals

differ in their dispositional positive affect in their everyday life, meaning some individuals

experience generally more and more intense positive emotions (Naragon and Watson, 2009),

and are thus also more likely to feel happy, excited, and active in school, which are aspects of

school engagement. Moreover, engagement and achievement are influenced by other

relatively stable individual characteristics, such as the academic self-concept and subjective

task values (Wang and Eccles, 2013), individual symptoms of burnout (Salmela-Aro and

Upadyaya, 2014), and ethnic background (Johnson, Crosnoe, and Elder, 2001), among others.

These influences help to explain why engagement remains partially stable within individuals

across situations, contexts and long time spans, even though engagement includes

nevertheless also malleable, fluctuating aspects (see below).

Intra-individual variation: situational aspects of engagement

Another characteristic of engagement is that it is malleable with situation-specific

components (Fredricks et al., 2004; Lawson and Lawson, 2013). These malleable components

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include the reasons why an individual approaches a task and how these tasks are experienced.

When an individual approaches a new task for the sake of exploration it is called situational

interest. This is a mostly externally triggered state in which the individual alertly focuses their

attention on the task (Bloom, 1985; Sloboda, 1990; Sosniak, 1990). The situational interest in

a task is an essential element that may lead to rewarding task experiences, such as feelings of

success, mastery, and confidence (Gruber, Gelman, and Ranganath, 2014), which may build

future motivation and engagement. Triggering situational interest may give instructors

opportunities to facilitate the development of engagement. Capturing the situational aspects of

engagement provides useful information to the specific mechanisms that can explain what

exactly goes on in the mind when students feel attracted to approach a task.

Contextual variation of student engagement

Contextual determinants of student engagement are of particular interest for teachers

and educational policy makers, for if identified, these conditions could be optimized to

increase students’ engagement. In their overview of the research on contextual determinants

of engagement, Fredricks et al. (2004) summarized that student engagement is higher in

schools and classes that provide students with opportunities for voluntary choice, participation

in school policy, and cooperation. Engagement can be encouraged by setting clear and

consistent goals, placing students in small classes, and holding students accountable for rule-

breaking. Regarding school and classroom characteristics, Wang and Eccles (2013) found that

contextual characteristics influence different facets of engagement (emotional, cognitive,

behavioral) differently, which makes it necessary to systematically distinguish between facets

of engagement when studying the context effects on engagement.

Broader contextual influences such as gender stereotypes and discrimination, cultural

norms, and social inequalities, problems related to family formation, childrearing, and work-

home balance can also influence student engagement, particularly among certain subgroups

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and in specific domains, as for example the achievement motivation of women in STEM

(science, technology, engineering, math) fields, where societal gender stereotypes about math

abilities, both positive and negative, can be further reinforced at home by parents (Wang and

Degol, 2013).

While prior research highlights these domain-specific inter-individual differences in

engagement, a gap in the engagement literature seems to be the question how engagement can

be measured in regard to specific domains (Fredricks and McColskey, 2012); and how and

why different domains or school subjects differ in terms of the engagement they elicit. It is

argued that some subjects like physics or math are experienced as particularly difficult and

frustrating by many students and by particular student subgroups (e.g., girls), and that the

detrimental effects of the person characteristics (e.g., gender) on student engagement are a

main reason for the recruitment problems of engineering and natural science sectors. As Wang

and Degol (2013) depicted in their review, important contextual influences for science

motivation and engagement are school and class characteristics, teacher characteristics, family

characteristics, peer influences and sociocultural influences.

Support provided by teachers is an important determinant of students’ engagement

(Fredricks et al., 2004; Wang and Eccles, 2013). Teachers can promote engagement by

supporting the students academically, emotionally and/or by supporting their autonomy while

learning. Teachers can also influence their students’ engagement by presenting topics in an

interesting way, setting challenging goals without overburdening the students, and providing

immediate and clear feedback. The teachers’ goals and mindsets, differential expectations and

stereotypes, and the students’ interpersonal relationships to teachers also contribute to

engagement at school (Wang and Degol, 2013).

Peers can influence engagement. Peer groups with pro-school and pro-learning

attitudes can promote engagement activities that are valued by their peers and give them

opportunities to relate to their peer group. However, this effect of peer’s values and attitudes

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on student engagement can be domain-specific and even detrimental for contrasting school

subjects. For example, peer support for math- and science-related school subjects

strengthened girls’ motivation in the same subject, but was negatively related to the same

girls’ motivation in English (Leaper, Farkas, and Brown, 2012). These contrasting effects

suggest that a domain-specific perspective is useful to understand the mechanisms behind peer

support and achievement motivation.

Integrating the analyses of person-specific, situational, and contextual aspects of student

engagement

One theory that helps to understand the situational, and contextual aspects of

engagement is flow theory (Csikszentmihalyi, 1990). Flow describes optimal learning

moments, i.e., situations when an individual concentrates so deeply on a task that the

cognitive resources are strongly loaded, and other cognitive processes such as self- and time-

monitoring are temporarily suspended. Flow experiences are often described as the feelings of

absorption and that ‘time was flying by’, which are also central components of engagement

measures (Salmela-Aro and Upadaya, 2012). In flow, individuals typically feel in control of

the current task, the activity is running smoothly, they know at all moments which current

steps they should take to master the challenge of the task, and feel completely lost in their

thoughts (Engeser and Rheinberg, 2008). Flow contributes to learning motivation because it is

a rewarding experience (Weber, Tamborini, Westcott-Baker, and Kantor, 2009), which elicits

positive affects (happiness, etc.), motivates students to concentrate deeply in a task at hand,

incentivizes the desire to re-engage in similar experiences in the future, and leads to a

productive outcome (e.g., learning).

Flow typically occurs in situations where an individual’s skills and level of challenge

are in balance— meaning the activity is cognitively demanding, but not overly difficult

(Csikszentmihalyi and Schneider, 2000). This conceptualization of a profile combining high

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levels of challenge and skill is different from many other approaches, which often

operationalize engagement as a composite score (e.g., average) across all its indicators, where

a high score in one variable compensates for low scores in other variables. Schneider et al.

(2016) added a third component to this conception, situational interest, defining situational

engagement as moments in which a student experiences high levels of skill, challenge, and

interest. The authors found that these states of situational engagement were correlated at the

same time with positive emotions as well as feelings of stress and anxiety.

The demands-resource perspective addresses similar predictors of engagement as the

flow theory, namely resources (flow: skills) and demands (flow: challenges; Bakker and

Demerouti, 2007; Tuominen-Soini and Salmela-Aro, 2014; Salmela-Aro and Upadyaya,

2014). Previous studies applied typically rather stable measures of demands (e.g., goal

challenge) and resources (e.g., self-efficiency) and found that study-related resources and

personal resources both predicted later schoolwork engagement, while school burnout was

predicted by previous study demands. Both school engagement and school burnout were

relatively stable, but school burnout had a detrimental effect of school engagement at a later

time point (Salmela-Aro and Upadyaya, 2014).

Recent studies on the intra-individual profiles of engagement and burnout and on their

intra-individual relations to demands and resources indicated that high engagement was a

positive experience only for some students, while other students experienced high engagement

together with strong exhaustion, burnout, and negative emotions (Salmela-Aro et al., 2016;

Tuominen-Soini and Salmela-Aro, 2014). Adult employees who experienced such co-

occurrences of high work engagement and high burnout also reported high levels of demands

(e.g., workload, cumbersome bureaucracy) and resources (e.g., self-efficacy, supervisor

support). Although the latter findings need to be replicated in the school context to become

relevant for the school engagement research, we can conclude that examining the intra-

individual profiles of engagement and burnout as well as demands and resources may help to

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understand and integrate the interactions of positive and negative experiences in relation to

engagement.

New directions in the assessment of engagement

The following paragraph discusses measures of engagement that capture the situation-

specific and domain-specific aspects and determinants of engagement. Furthermore, we

discuss how to operationalize the challenge-skill and demands-resources balance that are a

necessary pre-condition of (situational) engagement.

How to assess person-specific aspects of situational engagement

One of the shortcomings in most assessments of engagement is the fact that the

typically used measures do not tap well the malleable and domain-specific aspects of

engagement (Fredricks and McColskey, 2012). However, it is crucial to assess the situation-

specific fluctuation if the malleable aspects shall be addressed, and particularly if the

situational determinants of engagement shall be identified. Some measures assess aspects that

are situation-specific in theory, such as absorption and other aspects of flow, but with

questions that are administered only once to the students and therefore require the respondents

to aggregate in their minds how often they experience the questioned states (see Salmela-Aro

and Upadaya, 2012). This is a way to assess a general tendency in the overall frequency or

intensity in the experience of situational engagement aspects, but this measure is not suited to

tab situation-specific or domain-specific variation, which makes it difficult to study for

instance how particular instruction strategies or changes in classroom settings affect the

respective state aspects.

Because teaching and learning vary across subject (Stodolskey, 1988), it is important

to measure and contextualize engagement in domain-specific settings. Domain-specific

measures can help to answer questions such as how science engagement can be fostered

among students, which is of major interest for many researchers (see Wang and Degol, 2013).

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One possibility to assess domain-specific aspects of engagement is the use of scales that were

developed to measure specific subcomponents of domain-specific motivation. For example,

the Programme for International Student Assessment (PISA) measured engagement-related

aspects of motivation in relation to specific school subjects, with scales that in certain years

included subject-specific items for enjoyment, future-related motivation, general value, and

competence beliefs (OECD, 2007). These scales are translated into many languages and help

facilitate the study of student engagement in different countries. Furthermore, the

measurement properties of the scales and items are validated and well-documented. These

PISA engagement scales tap aspects of personal interest in given subjects and topics, but also

aspects of intrinsic and utility values per the expectancy-value theory, all of which are key

components of school engagement.

Lau and Roeser (2002) measured content-specific aspects of situational engagement in

science by administering questionnaires to the students shortly after they engaged in the

respective activities. The authors distinguished between situational test engagement (e.g.,

students’ mood and energy level, use of cognitive strategies and effort expended during the

test), situational classroom engagement (e.g., students’ self-reports of how much attention

they paid in class, their degree of participation in science experiments, the amount of

homework they completed for the class, and their use of self-regulatory strategies, when

studying science) and situational extracurricular engagement (e.g., student’s self-reports of

how often they engaged in various science-related activities outside school during their free

time, such as reading books and magazines, visiting web sites, or watching TV programs

related to science).

D’Mello et al. (2014) applied another measure of situational engagement by video-

recording their participants during a relevant learning activity and asking them at the end of

the learning session to watch their recorded video and to judge in retrospection which of the

emotions on a selected list described their experience best. The list included the experiences

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anxiety, boredom, confusion/uncertainty, curiosity, delight, engagement/flow, frustration,

surprise, and neutral, and a description for each of these experiences. Engagement was

defined to the participants as a state of interest that results from involvement in an activity.

Experience Sampling Method

One approach to measure situational engagement is the Experience Sampling Method

(ESM) developed by Csikszentmihalyi, Larson, and Bruce (1977), which attempts to capture

the malleability of engagement by contextualizing the experience of the individual. The ESM

provides a way of capturing the moments of an individual’s daily life—immediate activities

and emotions at random intervals. As opposed to collecting data at one time point and relying

on the study participant to recall/recount their engagement over multiple situations, the ESM

beeps or notifies a person to respond to a series of questions that take about one minute to

answer and asks for instances which activity the individual is doing (open-ended) and how he

or she feels about it (scaled item). The participants respond multiple times over the course of a

week or two to construct a more comprehensive dataset of how experiences (e.g., situational

engagement) vary. To study both short-term and long-term change in engagement, ESM

assessment weeks can be included in long-term longitudinal studies, for instances with an

ESM week in years one, three, and five as conducted by Schneider (1992-1997).

Several studies have established measures for separated components of situational

engagement, such as the measurement literature on situational interest (Chen, Darst, and

Pangrazi, 2009; Linnenbrink-Garcia et al., 2010; Schiefele, 2009); flow (Csikszentmihalyi and

Schneider, 2000; Engeser and Rheinberg, 2008); and state affect (Pekrun et al., 2011; Watson,

Clark, and Tellegen, 1988). The measures developed in these separated research lines can be

combined to tap several specific components of situational engagement at a time. However,

using the full battery of combined multi-item measures would be impractical in ESM studies,

since the burden of repeated measures requires short questionnaires. Therefore, ESM typically

assesses each component of engagement with a single item or very few items. With the

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advancement of technology and the ability of ESM to be used with smartphones including the

possibility of augmenting the responses with additional data such as heart rate, there are rich

opportunities for understanding the individual experience.

While there are certainly strengths to using the ESM, it is not without critique.

Fredricks and McColskey (2012) argue that the necessarily short and economic ESM

measures are often very limited to a few specific aspects of engagement and do not come up

to the whole spectrum of the multifaceted construct. The data collected at multiple points of

the day over multiple days can also be more burdensome for the respondent, which can lead to

selection bias and attrition in the study sample (Scollon, Kim-Prieto, and Diener, 2003). The

application of this method for studying student engagement in school could also be disruptive

for learning that is being measured. Although ESM collects real-time data, the data are still

self-reported and subject to the same limitations of any self-reported data, even though their

concurrent, discriminant and predictive validities were found to be good (Hektner, Schmidt,

and Csikszentmihalyi, 2007).

How to assess the balance between demands (challenge) and resources (skill)

If situational demands outweigh the individual’s current resources to deal with these

demands, then this can lead to a stressful state that the flow theory would identify as ‘anxiety.’

Other imbalances between situational demands and resources can lead to experiences of

relaxation (low demand and high resources) and apathy (low demand and low resources) —

all of which differ strongly from each other and from more balanced states (Csikszentmihalyi

and Csikszentmihalyi, 1988). It is therefore important to differentiate between the different

profiles of demands-resources balance in state-measures of engagement. One distinction

between such types of states was described by Csikszentmihalyi and colleagues

(Csikszentmihalyi and Csikszentmihalyi, 1988; Csikszentmihalyi and Schneider, 2000), who

operationalized flow-states as coincidence of flow pre-conditions (high skills and high

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challenges) by creating dichotomous dummy variables (1 = flow; 0 = no flow), where 1

represents states in which all indicators (here: skills and challenges) scored highly, and 0

represents all other combinations (e.g., high challenge and low skills). In future studies, this

dichotomization between high and low scores of flow indicators (here: challenge and skills)

should be done based on raw scores, and not as in previous studies based on intra-individual

z-scores, because otherwise ratings below the scale midpoint (i.e., ‘rather not’ statements)

may be categorized as ‘high ratings’ in some individuals (Moeller, 2015).

Discussion

New insights provided by situation-specific measures

Student engagement is a hot topic for motivation researchers, education policy makers,

funding agencies, and educators. The reasons for the broad interest on engagement are the

expectation that students’ motivation is an important and malleable precursor for students’

achievement, adjustment and well-being in learning institutions. Building on previous reviews

that argued student engagement research understudied the malleable aspects of engagement

(e.g., Fredricks and McColskey, 2012), this chapter provided an overview about different

methods to measure malleable and context-specific aspects of student engagement, and

directions for future research.

Situation-specific measures of engagement can show how much students live up to

their maximum of engagement in school settings as compared to other everyday life activities.

The more often applied cross-situational measures provide insights about inter-individual

differences in learning motivation, but situation-specific measures open a whole new field of

intra-individual comparisons of experiences in different contexts, which helps to find out for

instances which school subjects are more engaging for a given individual (or group of

individuals), and which specific lesson was more engaging than another.

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In addition to the contextual information, situational measures of engagement allow to

model short-term development, for example from day to day, which yields insights that

macro-time level scales cannot provide (Hamaker, Dolan, and Molenaar, 2005; Lichtwarck-

Aschoff et al., 2008; Mroczek, Spiro, and Almeida, 2003). One important feature of

situational measures is the possibility for the researcher to aggregate the cross-situational

frequency or mean level of engagement states across the repeatedly measured experiences in a

certain context. Compared to the prevailing cross-situational questionnaires that require

individuals to aggregate their ‘typical’ engagement across all experiences in their own minds,

the calculation by the researcher would be less biased by social desirability and memory

errors. Using repeated situational measures, the fluctuating state aspects and the rather stable

trait aspects of engagement can be disentangled with multilevel modeling of variance between

situations (within-level) and variance between individuals (between-level).

In addition, repeated measures with ESM can be used to model the influences of the

context on the engagement, and can be used to compare the different effects of different

contexts. This is an advantage compared to the prevailing cross-sectional questionnaires,

which are either relatively context-unspecific, or limited to only one or few contexts.

For the above discussed reasons, we assume that providing useful measures for

situational engagement is a necessary but not sufficient step on the way towards

understanding what can be done at school to optimize students’ motivation, achievement, and

well-being. The next necessary step would use these measures to identify the conditions for

optimal engagement and to evaluate interventions aiming at such optimization.

Subgroups of states and individuals

Prior research on student engagement relies predominantly on variable-oriented

approaches (analyses of covariation between variables), but increasingly, researchers have

pointed out that overall correlations between variables describe populations, but do not

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necessarily apply on the level of individuals (Bergman and Magnusson, 1997; Molenaar,

2004; Reizle, 2013). In response, recent studies have examined the differences between

subgroups of individuals and their engagement profiles, uncovering insights that would have

been overlooked with the correlation-based analyses. For example, using latent profile

analyses, Tuominen-Soini and Salmela-Aro (2014) described the unexpected subgroup of

engaged-exhausted students, who experienced high levels of both engagement and burnout

and were likely to become disengaged at later measurement time points. Similarly, situational

engagement can be accompanied by rather negative, anxious, demanding and stressful

experiences in some situations and students (Moeller et al., 2015; Schneider et al., 2016).

More research is needed to understand this potentially ‘dark side of student engagement’ and

the possibilities to help motivated students to maintain their engagement without getting

exhausted in this process.

Directions for future research

As previous studies concluded, the research on the multifaceted construct of

engagement requires models, definitions and measurements that distinguish between the

specific facets of engagement and their relation to the outcomes or predictors of interest (e.g.,

Eccles and Wang, 2012; Fredricks et al., 2004). In this chapter, we demonstrated why this

specification implies the necessity to disentangle more systematically the situation-specific,

person-specific and context-specific sources of variance of student engagement. Particularly

needed are theoretical models and empirical studies that would examine these three groups of

determinants jointly while distinguishing them systematically. Future studies should examine

these determinants’ unique and interacting influences on the engagement of students in order

to answer which determinant affects engagement for which students and under which

circumstances.

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Since self-reports might be biased by response styles or other contortions, future

studies should validate measures of situational engagement by investigating their relation to

and objective measures that go beyond self-reports, such as objective reports on achievement

outcomes (GPA, grades), psychophysiological data of e.g., stress and indicators of state affect,

or behavioral observations (see also the chapter by Cambria and Dicke in this volume).

Future studies should continue to look out for subgroups of students who experience

different patterns of engagement and burnout than the overall population-level trend suggests.

Understanding such person-specific or subgroup-specific patterns of engagement and burnout

is key to the individualized assessment and support that we ideally would want to see in

schools. The recent intra-individual findings suggest that interventions targeting optimal

learning motivation in schools might need to move away from a ‘one size fits all’ approach

towards the question of ‘what works for whom under which circumstances.’ Furthermore,

analyses of intra-individual profiles might also be interesting on the level of specific

situations, for instance if the in-the-moment profiles of skills and challenges and their

relations to emotions in the same learning situations are examined. We expect that these

approaches will not only reveal new theoretical insights, but may also help identifying the

students who are in need for support from their teachers and caregivers in order to learn how

to maintain and renew their resources and high levels of motivation without getting

overwhelmed, stressed, or burned out in the process.

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