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How elementary school students'motivation is connected to self-regulationSanna Järvelä a , Hanna Järvenoja a & Jonna Malmberg aa University of Oulu , Oulu , FinlandPublished online: 11 Jan 2012.

To cite this article: Sanna Järvelä , Hanna Järvenoja & Jonna Malmberg (2012) How elementaryschool students' motivation is connected to self-regulation, Educational Research and Evaluation:An International Journal on Theory and Practice, 18:1, 65-84, DOI: 10.1080/13803611.2011.641269

To link to this article: http://dx.doi.org/10.1080/13803611.2011.641269

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How elementary school students’ motivation is connected to

self-regulation

Sanna Jarvela*, Hanna Jarvenoja and Jonna Malmberg

University of Oulu, Oulu, Finland

(Received 3 December 2010; final version received 11 August 2011)

Empirical research reveals that students face difficulties engaging in learning andachieving their goals in a variety of learning contexts. To study effectively,students need to regulate their learning process. In spite of increased under-standing of cognitive aspects of self-regulation, motivational aspects of regulationhave not yet been thoroughly probed. This study investigates how motivation isconnected to self-regulated learning when elementary school students (N ¼ 32)study science in real classroom contexts using gStudy software. This was done by:(1) identifying students’ situated motivation during the learning process, (2)analyzing how the students with different motivational approaches activatedcognitive self-regulation in authentic learning situations across multiple learningepisodes, and (3) complementing the understanding with the students’ individualaccounts of their motivation regulation during the learning process. The resultsshow that there are qualitative differences in the self-regulation tactics used by thehigh- and low-motivation students as they study. Motivation is linked closely toactive self-regulation.

Keywords: motivation; self-regulated learning; learning; gStudy

Introduction

Empirical research reveals that students face difficulties engaging in learning andachieving their goals in a variety of learning contexts (e.g., Azevedo & Cromley,2004; Volet & Jarvela, 2001; Winters & Alexander, 2011). The importance forsustained engagement is growing, because at school and in their free time studentsare surrounded by competing demands for their attention. The students have tomake appropriate choices, prioritize, resist temptations, and plan their work andlives strategically. They need to focus and adapt their behavior and actions to fitthe demands of the situation. To overcome these difficulties, and to studyeffectively, students need to regulate their learning process. This study aims tounderstand motivation in self-regulated learning and explore how the studentsactivate self-regulation in authentic learning situations across multiple learningepisodes.

Self-regulated learning theory (SRL) concerns how learners develop learningskills and use learning skills effectively (Pintrich, 2000; Zimmermann & Schunk,

*Corresponding author. Email: sanna.jarvela@oulu.fi

Educational Research and Evaluation

Vol. 18, No. 1, January 2012, 65–84

ISSN 1380-3611 print/ISSN 1744-4187 online

� 2012 Taylor & Francis

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2011). Self-regulated learning refers to a learner’s deliberate planning, monitoring,and regulating of cognitive, behavioral, motivational, and emotional processestowards completion of an academic task (Winne & Hadwin, 2008). There is muchresearch evidence that self-regulated learners are active participants who effectivelycontrol their own learning experiences in many ways, including organizing andrehearsing information to be learned, and holding positive beliefs about theircapabilities, the value of learning, and the factors that influence learning (e.g.,Schunk & Zimmerman, 2008).

In spite of increased understanding of the cognitive aspects of self-regulation,motivational aspects of regulation have not yet been thoroughly probed. Motivationregulation is a key to successful SRL (Wolters, 2003). It consists of the means bywhich students’ select and manage goals and how they follow through whenchallenges arise as their learning unfolds. While goals set standards for studentachievement (Pintrich, 2000), motivation regulation strategies operationalize howSRL is applied. It is often assumed that once students have a good basicunderstanding of relevant strategies, they are prepared, but this is not the case. Manystudents are not able to apply effective learning strategies when they are needed andgive up in the face of difficulty (Winne & Jamieson-Noel, 2002). This is to say thatthose students who cannot realize adaptive motivation regulation fail.

Situated motivation

Currently, there is a strong understanding of self-regulation in learning, but how self-regulation develops in learning contexts, and especially how motivation regulationcontributes to it, is not understood well enough (Boekaerts & Corno, 2005). Earlierstudies have not elaborated on motivation regulation in learning contexts becausethey have not been conducted in real learning settings (e.g., classrooms). Also,mainstream methods in self-regulation research have favored self-reportingmeasurements instead of trying to understand the processes in self-regulation andmotivation regulation (Winne & Perry, 2000). Although studies emphasize thedynamic and adaptive nature of self-regulation, there is little evidence as to howstudents actually adapt and develop their learning strategies across studying sessionsas part of self-regulated learning (Perry & Winne, 2006). Recently, there has been anincreased interest in the development of self-regulated learning processes andmotivation in authentic contexts (Zimmermann, 2008), and a concept of situatedmotivation has been introduced (Volet & Jarvela, 2001). Many of the motivationalconcepts derive from experimental psychology and originally have focused on theself, while the contextual aspects have been in the background. Today, researchersare interested in how motivation is influenced, developed, and constructed situated inlearning contexts. They also focus on reciprocal relationships among individualmotivation and peers, learning environment, and culture (Jarvela & Jarvenoja,2011). Conceptualizing motivation in learning contexts builds upon the situatedlearning paradigm, viewing the process of learning as distributed across a learner andthe environments in which learning occurs, as well as the activity in which a learner isparticipating.

Jarvela, Volet, and Jarvenoja (2010) claim, however, that although recentresearch has recognized the importance of contextual aspects in emerging andsustained motivation, in methodological solutions the context is mainly conceived asa unidirectional source of influence on individual motivation. They suggest

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approaching research on motivation and self-regulated learning in a way that it cancapture its social, enacted, and process nature.

How to study the actual regulatory processes of self-regulated learning

There is a decade of empirical research and theoretical development in SRL, andthe earlier methods, questionnaires, and interviews have been successful indemonstrating significant cognitive and motivational predictions of students’academic outcomes (Zimmermann & Schunk, 2011). Studies of motivation in SRLhave considered the role of students’ motivational feelings and beliefs regardinginitiating and sustaining changes in their self-regulation of learning. In thosestudies, analyses have revealed a close relationship between key SRL processes andmany sources of motivation during the phases of learning (e.g., Kitsantas &Zimmermann, 2002). Hadwin, Winne, Stockley, Nesbit, and Woszczyna (2001)demonstrated empirically how responses to self-report items about study tactics,goal selection, and external resource use vary depending on study context. Wolters(1999) pointed out that learners regulate and monitor cognitive and motivationalaspects during learning processes. This implies that both the study tactic use(cognitive aspect) and achievement goals (motivational aspect) are responsive tothe learning situation.

The current interest is to examine SRL as a process and contextual phenomenondeveloping across multiple episodes, and it poses methodological challenges forresearchers (Hadwin, Jarvela, & Miller, 2011). The self-report protocols revealalmost nothing about how learners adapt tactics in authentic learning situations,maintain tactics used in relation to their goals, and knit them into an efficientstrategy of how self-regulation adapts across studying episodes (Winne & Jamieson-Noel, 2002). We argue that the research needs to: (a) augment self-reports of SRLwith fine-grained traces of actual student actions while they study and (b) examinerelationships among actual actions and learners’ reflections, self-evaluations, andcontextual interpretations of learning activities.

Our approach has been to consider SRL as an evolving process in the learningcontext (Jarvela et al., 2010). Our studies have been focused on the socioemotionalaspects of peer interaction and group learning and have illustrated how students’motivational accounts of the interaction contribute to changes in engagement(Jarvela, Jarvenoja, &Veermans, 2008; Jarvela, Veermans, & Leinonen, 2008). Inspite of efforts for online and microlevel analysis of self-regulated learning processes(Zimmermann, 2008), there is a need to seek new technology-based methods thatcompensate for the limitations of traditional methods in capturing motivationalvariation on the fly and relate these more directly to studying tactics that are theengines of achievement.

gStudy and trace data for investigating the role of motivation in self-regulatedlearning

As a reaction to this need, Winne and his colleagues (2006) developed the gStudycomputer environment. gStudy is an advanced multimedia learning system thatoffers cognitive tools students can use to operate on structured multimedia content,represented in hypertext mark-up language. However, gStudy is an empty shell, sodepending on the contents the students are about to study, the contents are being

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imported or created in gStudy (Perry & Winne, 2006). The tools in gStudy are beingresearched to investigate their capacity to help students learn more effectively, bypromoting metacognitive monitoring and enhancing self-regulated learning. Forexample, the students can create notes that prompt the students to activate forexample their prior knowledge. Also, the students can highlight the text withdifferent types of labels that inform the students about what are the pieces of the textthat need further elaboration. In addition to its capability to offer diverse support toenhance learning, gStudy also records traces of actual students’ actions as they study,such as the content selected and tool chosen to manipulate information. Forexample, when the students create notes, gStudy records to external database thetime and the duration when the note was created, the location about where the notewas made, and also contents of the note. Basically, each action of the student ingStudy is recorded in a log file, such as mouse clicking or scrolling the page up anddown. However, traces of the students cognitive tool use (e.g., note making,constructing a concept map, highlighting) are defined as artifacts of tactics orobservable indicators about cognition that students create as they engage in a task(Hadwin, Nesbit, Jamieson-Noel, Code, & Winne, 2007; Winne & Perry, 2000).

Traces of students learning with gStudy provide means to investigate thestudents’ tactic use when it emerges without interrupting the learning process.Moreover, trace data will show the students’ realized use of tactics ‘‘on the fly’’which would not likely be captured by any other method (Nolen, 2006; Perry &Winne, 2006). Trace data, for example, capture in detail each tactic students performwhen studying, such as their focus, how they build a comprehensive picture aboutthe topics, and how tactics are used when a mismatch between an outcome anddesired outcome is monitored (Hadwin et al., 2007). However, when the log filetraces are collected in authentic classroom settings over time, it is difficult to identifySRL episodes from other behavior (Nolen, 2006). That is why it is important tocombine trace data into other data sources that could provide additionalinformation how motivation regulation actually takes place. Generally, traces ofstudents’ tactic use have not been a widely used method when examining self-regulated learning. The most common method to use traces has been, for example,capturing frequencies and durations of recorded actions that are considered to reflectdifferent aspects of self-regulated learning (e.g., Azevedo, Cromley, Winters, Moos,& Greene 2005; Manlove, Lazonder, & De Jong, 2007). Yet, these statistical methodsdo not show how these actions are actually used when learning with computers. Thisis to say, they do not show the sequences of these actions or the most common sets ofactions considered as learning patterns the students typically use when studying. Theadvantage of investigating the most frequent learning patterns of the students is thatthey do inform precisely the most typical set of actions. However, they do not informabout the quality or the conditions when these actions take place. That is why it isimportant to gather multiple types of data to explore the ways in which students self-regulate their actions, actually work, and relate this information to students’subjective situation-specific interpretations that are involved in their regulationprocesses.

Aims

The aim of this study is to understand how motivation is connected to self-regulatedlearning in actual, varying learning situations. This is done by: (1) identifying

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students’ situated motivation, in terms of emotional state and motivational goals,during the learning process; (2) analyzing how the students with different situatedmotivational approaches activated cognitive self-regulation in authentic learningsituations across multiple learning episodes; and (3) complementing the under-standing with the students’ individual accounts of their motivation regulation duringthe learning process.

Participants and context

Thirty-four elementary school students (15 boys and 19 girls) from two differentclassrooms participated in this study. Their age ranged from 9 to 13 years (M ¼ 10years, SD 0.96). These students were chosen since their classroom teachersvolunteered the study. After choosing volunteered teachers, also the students’willingness to participate was asked. Except for one student, all the students from thetwo classrooms agreed to participate.

The students studied a science topic of ‘‘Vital conditions of life’’ for 2 months, forabout two to three hours per week with gStudy. The task instructions were ill-structured, and the task structure was open. Also, the students’ control over the taskwas emphasized by giving students choices over the order and pace on what, how,and when they would study in the gStudy. All the students’ learning material wasincluded in the gStudy.

Procedure

First, the students were instructed by the teacher to use and practice differentstudying techniques with the assistance of four different cognitive tools in gStudy.The students’ science kit included four cognitive tools, namely, ‘‘note,’’ ‘‘highlightwith labels’’, ‘‘concept map’’, and ‘‘glossary’’. The ‘‘note’’ included four differenttemplates that prompted students to (a) generate a question and an answer to thequestion, (b) contrast different concepts, (c) explain the meaning of terms andconcepts, and (d) explain observations. The ‘‘highlight’’ included three differenttypes of labels, namely, ‘‘I don’t understand,’’ ‘‘important information’’, and‘‘interesting detail’’. The concept map involved making connections betweendifferent types of notes, when the notes were created in a concept map view.‘‘Glossary’’, instead, included explanations of all the concepts related to the sciencetopics. The teacher gave a short instruction and illustrated how to use these tools,then asked the students to practice them when studying with gStudy. The teacheralso explained the purpose of each tool.

Second, the teacher instructed the students to think about and elaborate upon thecurrent learning situation before they started to work in gStudy. Each time thestudents logged into gStudy, they were asked to fill in the ‘‘Motivation scaffoldsheet’’ (see next section). This was done to increase the possibilities for activeregulation at the beginning of each studying session.

Methods

Motivation scaffold sheet

When the students studied with gStudy, their situational motivation was scaffoldedthrough a ‘‘motivation scaffold sheet’’. The sheet was designed to prompt students to

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think about their situated goals and the reasons for their current emotional state(Jarvenoja & Jarvela, 2005; Wolters, 2003) and to collect data about their contextualmotivation. The sheet consisted of three components, each reflecting the students’motivation in the specific situation: (1) student’s evaluation of his or her emotionalstate in the specific situation (on a scale negative , neutral , and positive ); (2)student’s ability or willingness to elaborate and explain the reason or source for thesituational emotions (an open question); and (3) student’s ability to recognize his orher priorities in terms of motivational goals (available options related to academicachievement, socioemotional aspects of the learning situation, or willingness toexpress the priorities). The students were prompted to recognize and report theirimmediate emotions and goals they experienced in their current learning situationevery time they logged into gStudy.

Trace data

Each time the students logged into the gStudy learning environment, it recordedtraces of everything they did when studying with gStudy in a local XML database(Winne et al., 2006). The recorded log file traces constituted a series of events,namely model and view events. A model event is when the student modifiesinformation, for example, highlighting information with a label or creating a note. Aview event is information about how the student views the contents of gStudy, suchas hyperlinks, glossary, or texts (Hadwin et al., 2007).

Interviews

After the last lesson, all of the students were interviewed individually for about 20minutes in a private room on school property. The semistructured interview includedquestions about the students’ learning process and self-regulation (e.g., How did youplan your studying?), motivational goals and motivation control (e.g., What wereyour goals and how did you manage to achieve your goals?), challenges during thelearning project and emotional control (e.g., What was the most challengingsituation in the task and how did you cope with it?). The interviewers tried to engagestudents in a discussion about their reflections on and interpretations of specificsituations in the 2-month science learning project. Students’ responses to thequestions were followed by elaboration questions from the interviewers, such as‘‘Tell me more about this’’ or ‘‘Can you give me an example?’’

Data analysis

The analysis proceeded in three phases. In the first phase, students’ motivation everytime they logged into gStudy was defined, based on responses to the ‘‘motivationscaffold sheet’’ (see Figure 1). In this study, three different components were chosenas indicators for motivation to learn in a specific situation. These three componentsare emotional state (Jarvenoja & Jarvela, 2005), awareness of the sources foremotion (Hadwin et al., 2011), and motivational goal (Linnenbrink, 2005), and theyare considered also as aspects of motivation that can be regulated during learning(Wolters, 2003). In the analysis, each of the three components of the sheet (student’sevaluation of his or her emotional state, student’s ability or willingness to explain thereason or source for the situational emotions, and student’s goal priorities in the

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specific situation) was rated as 1 or 0, with 1 indicating a favorable response in termsof motivation to learn at the time and 0 indicating an unfavorable response (seeTable 1). The first component, emotional state, was considered as favorable formotivation to learn in the specific situation when it was reported to be on a positiveside on a valence scale from negative to positive. The scale ranges from 0 to 100, 0indicating ultimate negative emotional state, 50 indicating neutral emotional state,and 100 indicating ultimate positive state. The scale also included three ‘‘happyfaces’’, one in each of the three cutting points of the scale. When the emotion valuereceives a value of 51 to 100, it was rated with 1, and the values from 0–50 were ratedwith 0.

The second component, explanation for emotional state, was an open questionwhere the students were asked to explain or elaborate on the reasons and sources forthe current emotional state. The coding of this component included a basicassumption that if the student was willing or able to relevantly explain these reasons,it is considered as a favorable base for motivation to learn in that situation. This is

Table 1. Rating of the different components of students’ responses to the ‘‘motivationscaffold sheet’’.

rate 0 1

Emotional state negative ( ) or neutral ( ) positive ( )Explanation for emotion irrelevant or no answer relevantSituational goal priority related to socioemotional

aspects/unable or unwillingto express the priorities

related to academicachievement

Total range of situationalmotivation

0j– low ––––––

3–––––– high –j

Figure 1. Motivation scaffold sheet.

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because the awareness of the reasons for emotional state makes it possible to bettercontrol or change the conditions when needed. Hence, the response was rated with 1if it included a relevant explanation such as ‘‘it is fun to start working’’ (related topositive emotional state) or ‘‘because I don’t know how to use the computer’’(related to negative emotional state). The response was rated with 0 when there wereno explanations provided or the explanation was ‘‘I don’t know’’ or somethingirrelevant such as only ‘‘because’’.

The third component, situational goal priority, was considered as favorable formotivation to learn in the situation if it emphasized academic performanceand learning at hand. The goal was chosen from a list of different goal items, andit was coded with value 1 if the student chose a goal directly related to learning oracademic achievement. If the chosen goal was related to other areas of life, such as‘‘have fun’’, ‘‘not stress’’, ‘‘do the same as others’’, and ‘‘to be satisfied’’, it was codedas 0.

The separate coding of each component resulted in a value ranging from 0 to 3for the student’s motivation in a situation in respect to the learning task. Next, theresponses were divided into three categories: low (0–1), average (2), or high (3)motivation in the situation, based on the number of components realized. In otherwords, students’ situational motivation was determined from three differentcomponents, emotional state, awareness of the reasons for the emotional state,and personal goals in the specific situation.

Finally, the students who had a low or high situational motivation were located.A student was considered to possess low or high situational motivation if he or shegot a score of 0–1 (low) or 3 (high) in over fifty percent of the responses. This wasdone in order to be able to focus on those students’ trace data and interviewexplanations who repeatedly seem to produce a high or low motivation in specificlearning situations. The assumption was that all students’ situation-specific emotionsand goals vary from situation to situation, but that concentrating on the two groupsof students that most often begin their studying either highly or poorly motivatedmakes it possible to better locate differences from their learning process. Altogetherthere were 11 students whose responses met this requirement: 5 ‘‘low situationalmotivation students’’ and 6 ‘‘high situational motivation students’’. These studentsformed two groups for further analyses.

The second phase of the analysis focused on trace data to identify all the tactictypes used by the students with the assistance of four cognitive tools (see Table 2).The students used nine different tactic types: creating a new concept map; creating a

Table 2. Cognitive tools and tactic types.

Cognitive tools Tactic types

Concept map Create new concept mapCreate note in a concept mapLink notes in a concept map

Note Create note in a note viewCreate note in a browser

Highlight Label: Important informationLabel: Interesting detailLabel: I don’t understand

Glossary View glossary

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note in a browser view; creating a note in a concept map; linking notes in a conceptmap; creating a new note in a note view; viewing the glossary and using threedifferent types of labels, such as ‘‘important information’’, interesting detail’’, and ‘‘Idont understand’’. This is to say, the students had nine different tactic types availablein gStudy, but typically all the students used five different tactic types (Mdn ¼ 5,Std ¼ 1.7) in each session.

After the tactic types were identified, the overall activity of all the students ineach session was defined by dividing the frequency of tactic types (Mdn ¼ 11,Std ¼ 11.9) with the duration of the uses of these tactic types (Mdn ¼ 17.35,Std ¼ 9.3). In order to investigate differences with high- and low-motivated students’uses of different tactic types, frequency of tactic use and activity in each gStudysession, a non-parametric Mann-Whitney U Test was selected.

Next, specific learning patterns that were common for both high- and low-motivated students were explored, and the frequency of the occurrence of tactic typesin these patterns was decoded from both groups separately. Along with gStudylearning environment, an analysis tool has been developed (Xu, Nesbit, Zhou, &Winne, 2007) that allows investigating with data parsing and data mining techniquehow students actually use different tactics when learning and also duration of thistactic use. Data mining technique allows investigating what are the most commonsets of tactics, considered as learning patterns, that different groups of studentstypically use. These techniques allow us to compare and contrast differences (orsimilarities) of the realized tactic uses of the students (Nesbit, Zhou, Xu, & Winne.2007).

In this study, the proportion of the learning pattern was considered as a set offour tactics. This is because strategies are composed of a set of tactics, but it does notmean that all the uses of tactics are necessarily strategically applied (Winne, 2001).However, a proportion of four tactics is considered to cover (a) patterned use oftactics and (b) patterned variations in the students’ uses of tactic types. Since thenumber of log files varied across all the students, the learning patterns wereconducted by merging three log files from each student, respectively. The selected logfiles were taken from the first, the last, and the middle gStudy sessions from all thestudents. Since the result of data mining was thousands of learning patterns fromboth groups, the longest learning patterns were selected as examples from bothgroups since these learning patterns do overlap. This means that a shorter pattern isincluded into the next longer pattern until the saturation point is reached. Finally,the criteria for selecting representative and similar learning patterns from bothgroups were: (1) the longest learning pattern that would characterize patterned use oftactics and (2) the longest pattern that would characterize changes in the students’uses of tactic types.

The third phase of the analysis examined student interview data. First, therecorded interview data were transcribed. The data analysis followed an iterativeprocess, which began by reading the interview transcripts and trying to identifythemes. Two main categories emerged: cognitive self-regulation and motivationregulation. The cognitive self-regulation category included students’ reflections ontheir strategic activities during their learning process (e.g., goal setting, planning,elaboration, and metacognitive monitoring). The motivation regulation categoryincluded students’ reflections on the motivation and emotion control while studying(e.g., interest enhancement, efficacy management, performance, or mastery talk).Both of the categories included positive and negative dimensions, which means that

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the students were either able to express self-regulation and/or motivation control(e.g., ‘‘I know I have to be patient if I have a challenging task, and I did it well here’’)or not (e.g., ‘‘I don’t know whether I had any goal’’).

Results

Students’ situational motivation profiles

Each time the students logged into gStudy, they were instructed to start the work bycompleting a motivation scaffold sheet. The number of responses for each studentvaried between 2 and 12, resulting altogether in 263 responses. These responses weredivided into three categories, namely, low, average, and high situational motivation.Table 3 shows the proportion of the responses among the three groups. Twenty-seven percent of all the responses fall into the low category and 27% into the highsituational motivation category.

If over fifty percent of a student’s responses fall into either the low or high group,they were identified for further analyses (see Table 4). The low-motivation group hadfive students, with responses that varied between 50 and 100% of all of theirsituational responses. These responses represented 29% of all low-motivationcategory codings. The high-motivation group consisted of six students, and theirhigh situational motivation responses varied between 60 and 100% of all of theirsituational responses. These responses represented 48% of all the high-motivationcategory codings.

Table 3. Frequencies and proportions of the responses.

Situational MOTIVATION f %

LOW (value 071) 69 27AVERAGE (value 2) 122 46HIGH (value 3) 72 27TOTAL 263 100

Table 4. Students with low or high situational motivation.

Name Motivation group % of responses in motivation group

Hanna low 50Jukka low 100Seppo low 67Sari low 100Taneli low 50

n ¼ 5Henna high 60Ismo high 67Jaana high 86Pekka high 60Saara high 78Susanna high 100n ¼ 6

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Characteristics of tactic use among high- and low-motivated students

All the students (n ¼ 33) logged in the gStudy 242 times. The students from the low-motivation group (n ¼ 5) logged into the gStudy a total of 33 times, and the studentsin the high-motivation group (n ¼ 6) logged in altogether 50 times. A nonparametricMann Whitney U test was used to analyze differences between the low- and high-motivated students’ uses of different tactic types, duration of tactic uses, and activity(tactics per minute) in each gStudy session. The test results revealed that there wereno statistically significant differences between the two groups uses of tactic types(U ¼ 763, z ¼ 7.806), duration of tactic uses (U ¼ 701, z ¼ 71.154), or activity intactic use (U ¼ 655, z ¼ 71.58).

The high-motivated students typically used five different tactic types in eachsession. The duration of tactic uses was 18 min and 46 s. Also, the students used .78tactics per minute. The low-motivated students typically used 4.6 tactic types in eachsession. The duration of tactic uses was 16.63 min, and the students used .63 tacticsin a minute. This means that the low-motivated students spent less time when usingtactics, and, moreover, their uses of tactics were more frequent when contrasted tohigh-motivated students.

Table 5, however, shows that students in the high-motivation group scored aboveaverage means of the low-motivation group when considering the variety of tactictypes that were used, duration of tactic use, and activity in tactic uses. Also, the high-motivated students scored above average means of the whole sample size whenconsidering the variety of tactic types and activity in tactic uses. The low-motivatedstudents were below the average mean of the whole sample size when contrasted tovariety of tactic types, duration of tactic uses and activity.

From the high-motivated students’ tactic uses emerged altogether 15,428 learningpatterns, whereas from the low-motivated students’ tactic uses emerged 2,780learning patterns (see Table 6). From both high- and low-motivated students’learning patterns, four tactic types were found, namely, ‘‘Label: Importantinformation’’, ‘‘Label: Interesting detail’’, ‘‘note in concept map’’, and ‘‘link inconcept map’’. First, the occurrence and frequency of tactic types that occurred inboth groups’ learning patterns was explored. In 97% of the learning patternsoccurred a tactic type ‘‘link in c-map’’ from both high- and low-motivated students.This means that both high- and low-motivated students’ tactic use was focused onconstructing a concept map.

However, from 79% of the learning patterns that emerged from high-motivatedstudents’ occurred tactic type ‘‘Label: Important information’’. This means that thehigh-motivated students used this tactic type very often along with constructing aconcept map. Other tactic types, such as ‘‘Label: Interesting detail’’ occurred in 17%of the learning patterns. From 24% of the learning patterns that emerged from low-motivated students occurred tactic type ‘‘Label: Interesting detail’’. Other tactic types,such as ‘‘Label: Important information’’ occurred in 25% of the learning patterns.This means that low-achieving students were focused on constructing a concept map,and other tactic types were seldom used.

In order to demonstrate how high- and low-motivated students’ learning patternswere composed, examples of learning patterns are presented in a graphical form(Figures 2 and 3). On the X-axis, the order of different tactics and the length of thelearning pattern are presented, whereas on the Y-axis, all the tactic types thatoccurred in a selected learning pattern are presented in numerical order. The tactic

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Table

5.

Therangeandmeansbetweenthehigh-andlow-m

otivatedstudents’usesoftactic

types,durationoftactic

use,andactivityin

tactic

use.

Highmotivation(f¼

50)

Low

motivation(f¼

34)

Whole

sample

(f¼

242)

Range

Min.

Max.

MRange

Min.

Max.

MRange

Min.

Max.

M

Tactic

types

62

85

52

74.6

72

94.85

Durationoftactic

use

34.80

3.11

37.90

18.46

31.80

5.68

37.48

16.63

42.47

3.11

45.57

18.82

Activityin

tactic

use

2.33

.16

2.49

.78

1.11

.18

1.89

.63

2.34

.14

2.49

.70

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types are 1 ¼ Label: Important information, 2 ¼ Link notes in concept map,3 ¼ Make note in concept map, and 4 ¼ Label: Interesting detail. The selectedlearning patterns from both groups were (a) the longest learning patterns and (b) thelearning pattern where all four tactic types occur. The longest learning pattern fromboth groups will characterize typical use of tactics. The learning pattern where allfour tactic types are occurring will characterize variations in both groups’ uses oftactic types.

The first example illustrates how high- and low-motivated students typically usedtactics (Figure 2). From both high- and low-motivated students, the longest learning

Figure 2. Contrasting the longest learning patterns between the high- and low-motivationgroups.

Table 6. Tactic types that constitute learning patterns and number of learning patternsamong high- and low-motivated students.

High-motivationgroup learning

patterns

Low-motivationgroup learning

patterns

Tactic types F % F %

Label: Important information 12,133 79 697 25Label: Interesting detail 2,640 17 682 24Make note in c-map 15,238 99 2,691 97Make link in c-map 15,023 97 2,689 97Total number of learning patterns 15,428 100 2,780 100

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pattern was focused on constructing a concept map. All the high-motivated students(6/6) used the selected learning pattern, whereas (4/5) low-motivated students usedthe selected learning pattern. However, the high-motivated students’ learning patternstarted by labeling ‘‘important information’’ before constructing a concept map,whereas the low-motivated students’ learning pattern was focused only onconstructing a concept map. This means that high-motivated students first selectedthe ‘‘important’’ information and then carried out a deeper strategy which wasfocused on constructing a concept map, whereas low-motivated students’ learningpatterns were very often repetitious (e.g., repeating ‘‘making links’’ and ‘‘makingnotes’’).

The second example illustrates variations in high- and low-motivated students’tactic uses (see Figure 3). In the selected learning patterns, both high- and low-motivated students use all four tactic types that occurred in learning patterns. Thesetactic types were ‘‘Label: Important information’’, ‘‘Label: Interesting detail’’, ‘‘notein concept map’’, and ‘‘links in concept map’’.

High-motivated students start labeling three times successively ‘‘importantinformation’’ and then start to construct a concept map. This is done by making twonotes and then linking them together. However, after constructing a concept map,the high-motivated students again label ‘‘important information’’ and ‘‘interestingdetail’’. This indicates that high-motivated students were consistent in their uses of

Figure 3. Contrasting the longest learning patterns where all four tactic types occur betweenthe high- and low-motivation groups.

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tactics. Low-motivated students started labeling ‘‘important information’’ and then‘‘interesting detail’’ two times successively and then started to construct a conceptmap. However, next, only one new note and three links are generated. This mightmean that a note that was just generated was then linked with three other notes thatalready existed. Both high- and low-motivated students used two types of labelsbesides constructing a concept map. However, the high-motivated students wereconsistent in their tactic uses – learning patterns from high-motivated students’started and ended using ‘‘labeling’’ as a tactic type, after constructing a concept map.This is to say, that high-motivated students were committed in using different tactictypes, and, moreover, they were consistent with their tactic use.

In sum, the two groups were distinguished not only by the length and number oflearning patterns but also by the order and variety of tactic types in their learningpatterns. It seems that the high-motivated students’ actual use of these three differenttactic types was diverse. The high-motivated students switched between labeling andconstructing a concept map, started by selecting the important information beforeconstructing a concept map, and returned back to selecting relevant information.Low-motivated students instead remained at constructing a concept map andseldomly selected information by using labels as a tactic type before constructing aconcept map.

What kind of role does motivation regulation play in students’ self-regulation?

The interview data and students’ individual accounts were used in order to identifywhether the students themselves indicated an ability to actively self-regulate whilethey study. Altogether, there were 148 indications of self-regulation in the interviewdata. The analysis of the interviews revealed two categories of regulation: cognitiveregulation and motivation regulation. Each of the categories involved positive and/or negative dimensions; the students were either able to explain how they activatecognitive self-regulation and motivation regulation, or they expressed that they didnot activate these processes.

When asked how the student helped herself to understand the task, an example ofstudents’ positive cognitive regulation is ‘‘I begin thinking . . . kind of like activatingmy mind. What I should do next, and so on’’, and an example of negative cognitiveregulation is ‘‘I did not plan anything, I just opened the computer.’’ Dealing withmotivation regulation, the students were asked, for example, what they did in adifficult situation. An example of a positive motivation regulation is ‘‘I said to myselfthat, yes, I can finish this task, I only have to concentrate on the task and not to chatwith my friends.’’ Negative motivation regulation is illustrated in an answer whenthe student was asked to explain what he did to help himself to focus on the taskwhen tired: ‘‘I don’t know . . . Nothing, there was nothing to do then’’.

The interview data analysis for cognitive regulation (82 indications) reveals thatamong the high-motivated group of students, 69% of the answers were positivecodings, while 31% were negative codings. Low-motivated students’ answers were31% positive codings and 69% negative codings. Thus, the high-motivated group ofstudents said they were more active in cognitive regulation than the low-motivationgroup of students.

When considering the motivation regulation in the interview data (64indications), among the high-motivated group of students, 66% of the codingswere positive and 44% negative. Among the low-motivated group of students, 33%

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of the codings were positive, while 56% were negative. The high-motivation group ofstudents were, therefore, more active in their accounts of motivation regulationduring the learning project.

Conclusions

This study investigated how motivation is connected to self-regulated learning whenelementary school students studied science in real classroom contexts with gStudysoftware. The data collection focused on situated information about studentmotivation and self-regulation in terms of actual traces of students’ learningprocesses during the task execution and interview accounts about the learningprocess. First, the students’ situated motivation during the learning process wasidentified according to their responses to the motivation scaffolding sheet. It wasseen that the students’ situational motivation varied between situations (Turner &Patrick, 2008). The summarized results show that three different groups emerged interms of their situational motivation and two groups of student – high motivationand low motivation – were used for a more detailed analysis of their self-regulation.

Second, it was analyzed how students with different motivational approachesactivated cognitive self-regulation in authentic learning situations across multiplelearning episodes. The trace data analysis resulted in multiple data and found thatthe high- and low-motivated groups did not differ in activity, such as choices ofactions, duration of actions, action types, or activity in their gStudy sessions.Instead, there were differences between the high- and low-motivation student groupsin whether and how these actions were used repeatedly in the same way. Thispatterned learning activity provides evidence of students’ on-task actions and cancomplement the data that rely on students’ interpretations of their self-regulatedlearning process (Winne, 2004b). It may also indicate differences in differentstudents’ strategic activity (Hadwin et al., 2007).

Overall, the results of the study showed that the quality and the content of thelearning patterns were different for the two groups. High-motivation group studentsproduced more patterns containing chains of study tactics, whereas the low-motivation group students produced fewer patterns that were shorter and narrowerin terms of strategic activity. This is in accordance with the findings showing thatstudents’ motivational orientations are associated with self-regulated strategies(Pintrich & DeGroot, 1990) and that mastery-oriented students use deeperprocessing strategies than students stressing performance goals (Nolen, 1988).What is new in the findings of this study is that the trace data can ‘‘illustrate’’ thequality of students’ strategic activity. In this case, it was seen that the high-motivatedstudents were more exploratory in terms of variety of tactic use than the low-motivated students.

Third, interview data were used to complement the understanding of students’cognitive regulation and motivation regulation during the learning process. Theinterview accounts show that the high-motivation group students were more active intheir accounts of motivation regulation and cognitive regulation than the low-motivation group students. This is to say that all the students were active in thegStudy learning environment, but when the results from three different perspectives(situated motivation, actual strategic actions, and interview accounts) aresummarized, there is a distinct difference between how the high- and low-motivationgroups self-regulated their learning while they studied. It was also seen that students’

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motivational approaches are linked to students’ activeness in self-regulation, andespecially motivation regulation (Jarvela et al., 2008; Wolters, 1999).

Self-regulation, and especially regulation of motivation, is seldom studied inlearning contexts, and changes over time (Winne, 2004a). Lately, this challenge israised by several researchers who have started to develop, use, and refine severalsituation-specific, context-bound methods ‘‘on-the-fly’’ measures (e.g., Ainley &Patrick, 2006; Boekaerts, 2002). In this study, situational motivation and cognitivelearning processes were considered as contextual indicators and evidence of astudent’s willingness and success in motivation regulation. The study demonstratedhow motivation has an important role in successful self-regulated learning. Also,previous studies have shown the importance of motivation and emotion regulation inlearning, but to date the research has not provided clear results of the processes thatcan be supported (Boekaerts & Corno, 2005). This study emphasizes that especiallystudents that are often poorly motivated need support to activate their regulationprocesses.

Because of the contextual and situated nature of data collected in this study,students’ individual characteristics have not been considered in detail, which gives alimited perspective to the individual differences in motivation and self-regulation inaction. Further studies should implement more information about individual studentcharacteristics, but there is already a strong argument for studies that aim tounderstand better how motivation and regulation of motivation are constituted inreal learning situations and how motivation regulation can be scaffolded. Thecontext-specific nature of data collection reveals details of students’ cognitive andmotivational processes in real situations, but also makes the results less generalizedto other contexts. More research is needed to achieve cumulative cross-case findings.

This study contributes to the ongoing theoretical and methodological discussionon motivation and self-regulated learning. First, it contributes to the empiricalfindings of motivation as a contextual phenomenon (e.g., Jarvela et al., 2010) butespecially to the new research designs and analysis. Until now, research about SRLhas relied heavily on self-report instruments and performance measures toinvestigate the influence of social and contextual factors in individual self-regulation(Pintrich, Wolters, & Baxter, 2000) but failed to capture the fine-grained, dynamicadaptation learners make within and across studying sessions that defines SRL(Cleary, 2011). In this study, fine-grained contextual data collection and qualitativeanalysis were implemented in order to find out how students’ motivationalinterpretations interact with their SRL in specific learning situations.

Following the methodological approach of this study in future, more detailedanalyses are needed to discover some indicators of critical situations in self-regulatedlearning processes. In practice, this information can be used to scaffold motivation,and especially motivation regulation, in self-regulated learning. As social awarenesstools (e.g., Phielix, Prins, Kirschner, Erkens, & Jaspers, 2011) and cognitiveregulation support tools have been developed in earlier research (Manlove et al.,2009), student’s awareness of motivation (e.g., goals) and their role as activeregulators of learning could be prompted by technology tools. Methods such as amotivation scaffold sheet (Jarvenoja, Volet, & Jarvela, 2011) provided a way toincrease both teacher and student awareness of emotional and motivational aspectsthat are situation specific. This type of information can be channeled to scaffoldlearners’ regulation processes and support them to become active agents in their ownlearning.

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Acknowledgement

The funding of this study is supported by the Finnish Science Academy grant number1107734.

Notes on contributors

Sanna Jarvela, PhD, is a professor in the field of learning and educational technology andhead of the Learning and Educational Technology Research Unit (LET) (http://www.let.ou-lu.fi/) in the Department of Educational Sciences, University of Oulu. Her main researchinterests deal with learning processes in technology-enhanced learning, social and motivationalprocesses in learning, and self-regulated and computer-supported collaborative learning.

Hanna Jarvenoja, PhD, is a post-doctoral researcher at the Learning and EducationalTechnology Research Unit (LET), University of Oulu. Her research interest is in self-regulatedlearning, especially in emotion and motivation regulation in individual and socially sharedlearning situations.

Jonna Malmberg, MEd, is preparing her dissertation at the Learning and EducationalTechnology Research Unit (LET), University of Oulu. She is interested in self-regulatedlearning, cognitive aspects of regulation, and how technology tools can support solo andcollaborative regulation.

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