Computers in Human Behavior 26 (2010) 1034–1042

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Computers in Human Behavior

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Tracing elementary school students’ study tactic use in gStudy by examininga strategic and self-regulated learning

Jonna Malmberg *, Hanna Järvenoja, Sanna JärveläDepartment of Educational Sciences and Teacher Education, University of Oulu, P.O. Box 2000, FIN-90014, Finland

a r t i c l e i n f o

Article history:Available online 2 April 2010

Keywords:Self-regulated learningStudy tacticLearning strategygStudyComputer supported learningElementary school students

0747-5632/$ - see front matter � 2010 Elsevier Ltd. Adoi:10.1016/j.chb.2010.03.004

* Corresponding author. Tel.: +358 8 553 3725; faxE-mail address: jonna.malmberg@oulu.fi (J. Malmb

a b s t r a c t

This study investigated, with the help of log file traces (f = 172), how 20 elementary school students usedstudy tactics when studying science within the gStudy learning environment and examined how tacticuse contributed to the students’ achievement. The analysis of this study is divided into two parts. First,at the situational level, the focus is on capturing the tactics that were used in different gStudy sessions,classifying the gStudy sessions based on the tactic use, and illustrating the patterned use of tactics duringthese sessions. Second, at the individual level, the focus is on examining individual students’ typicalmethods of using tactics, which helps to illustrate how tactic use contributes to the students’ achieve-ment. The gStudy sessions were classified into three categories on the basis of tactic use: rare, moderate,and frequent. Findings indicate that frequent tactic use did not contribute to deep learning. Moderate tac-tic use was fairly effective for learning, but rare tactic use contributed to deep learning. The results didnot show that the use of many study tactics improves learning; rather, they suggest that the distinguish-ing feature in strategic learning is not the tactic use itself but the way the tactic is performed.

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1. Introduction

Hypermedia learning environments are gaining more and morepopularity in education, especially for acquiring information. Forexample, the World Wide Web is often used in education to helpstudents learn about complex topics, with a range of informationbeing presented in the form of text, graphics, animation, audio,and video (Jacobson & Azevedo, 2008). Hypertext documents arenonlinear and open ended; they enable students to follow theirown learning paths (Puntambekar & Stylianou, 2005). Searchingand navigating are the main activities that students perform,which involve setting of specific goals for learning-related deci-sions, i.e., what to learn, how to learn, how much time to spendon learning, and how to determine whether learning has beenassimilated. Hypermedia environments have the potential to bepowerful learning tools, but still, majority of studies have shownthat learning with hypertext and hypermedia often leads to verylittle learning if students do not self-regulate their learning(Azevedo, Cromley, Winters, Moos, & Greene, 2005).

The skill and will to self-regulate learning are emphasized whenhypermedia are used, especially if the learning tasks are ill-struc-tured and unclear (Azevedo et al., 2005). In the literature, self-reg-ulated learning is often described as an active cyclical processwhereby students regulate their efforts to optimize cognitive,

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motivational and behavioral processes, guided by their learninggoals and the contextual features of the environment (Pintrich,2000; Zimmerman, 1998). Self-regulated learning includes ele-ments of planning, goal setting, monitoring, and controlling theprogress toward the achievement of a learning goal. Different stud-ies have focused on different aspects of self-regulation such asmetacognitive processes (Winne, 1996), learning strategies (Paris,Byrnes, & Paris, 2001; Weinstein, Husman, & Dierking, 2000) andmotivation regulation (Wolters, 2003). Despite the differences inthe theoretical aspects, all the studies have contributed to describ-ing the important features of successful learning when confrontedby situation-specific challenges (Järvelä & Niemivirta, 2001).

Many studies have shown that students face difficulties whenstudying within hypermedia learning environments (Jacobson &Azevedo, 2008; MacGregor & Lou, 2004). Students do not deployself-regulatory processes such as planning and creating goals forlearning, nor do they activate their prior knowledge in a meaning-ful way to anchor their learning to previously learned material. Inaddition, the learning strategies used by students tend to be super-ficial, such as copying information without determining whetherthey understand the information or not (Azevedo et al., 2005;Salovaara & Järvelä, 2003). Several studies have acknowledgedthese challenges and have attempted to help students activateand use their self-regulatory processes with assistance from tea-cher, peers or computer software (Jacobson & Azevedo, 2008; Mac-Gregor & Lou, 2004; Puntambekar & Hübscher, 2005; Winne et al.,2006). For example, computer-based learning environments can

J. Malmberg et al. / Computers in Human Behavior 26 (2010) 1034–1042 1035

use embedded system features such as prompts and hints to sup-port self-regulated learning (Puntambekar & Hübscher, 2005;Winne et al. 2006).

Log file traces have not been the focus of study tactic and strat-egy use in self-regulated learning (SRL), which has conventionallyrelied on self-reports when such tactics have been investigated(Bråten & Samuelstun, 2007; Hadwin, Nesbit, Jamiesson-Noel,Code, & Winne, 2007). Self-report data, which measure SRL as anaptitude or trait, target students’ own perceptions or explanationsof the strategy use (Winne & Perry, 2000), although students mightuse different strategies depending on the task or learning situation.The problem is that self-report data do not answer questions abouthow students actually use study tactics while learning and how aset of tactics strings out a strategy (Hadwin et al., 2007; Winne,2006).

Capturing self-regulated use of study tactics ‘‘on the fly” re-quires recordings of actual studying events. When self-regulatedlearning is studied as an event, the learning can be accomplishedat three levels, namely, occurrence (monitoring a need to imple-ment, apply, or adjust a tactic), contingency (applying a study tac-tic), and patterned contingency (several tactics arrayed ascognitive strategy) (Winne & Perry, 2000).

Log file traces gathered from the gStudy learning environmentare a recent innovation in SRL research. gStudy is an advanced mul-timedia learning environment designed based on prior research onself-regulated learning. gStudy includes cognitive tools that stu-dents can use when practicing study tactics. It also helps studentsexplore learning strategies while studying with hypermedia. Inaddition, the tools aim to enhance students’ cognition and meta-cognition in self-regulated learning (Winne et al., 2006). As learn-ers use cognitive tools, gStudy records traces of all the students’actions such as what content was selected, when the selectionwas made, and what tool the student used. These constitutedtraces of actual students’ actions are recorded in the log files.Traces are defined as artifacts of tactics or observable events ofcognition that students create as they engage in a task (Hadwinet al., 2007; Winne & Perry, 2000). The advantage of log file tracesis that they present a new opportunity for examining SRL as anevent without interrupting the actual learning performance.

Log file traces are essential for studying SRL with children, sinceself-report data are developmentally inappropriate. Children facedifficulties in generalizing their actions across more than one event(Perry & VandeKamp, 2000). Young children also want to presentthemselves in the best possible light, and they might not be ableto distinguish their effort and intentions from their actual behavior(Paris & Newman, 1990).

Taking into account the limited understanding of how studentsuse study tactics and act strategically in actual learning situations,this study traced a process of elementary schools students’ self-regulated use of study tactics in different learning situations. Anadvantage of this study is the use of gStudy software, which re-cords and stores log file traces of students’ actual use of study tac-tics ‘‘on the fly” as they study. The log file traces show the studytactics that are actually employed by students while studying,and they extend beyond the students’ own perceptions about theways they believe they study (Hadwin et al., 2007). Using gStudysoftware to examine tactic use in actual learning situations enablesus to explore how the use of study tactics contributes to students’achievements.

2. Regulation of cognition – Learning strategies and studytactics

The central aspect of the regulation of cognition is the adaptiveuse of learning strategies and study tactics in various learning

situations (Pintrich, 2004; Winne, 2006). Regulation of cognitioncan be classified into four different phases: goal setting and plan-ning, monitoring, controlling, and reflection (Pintrich, 2000). Dur-ing each study phase, self-regulating students make cognitiveevaluations about the discrepancies between the actual and de-sired accomplishment. As a result, students might change or adaptcognitive operations to accomplish a learning goal, which is thehallmark of self-regulated learning (Winne & Hadwin, 1998,2008). Cognitive operations that are considered to be studying tac-tics are mainly carried out during the control phases, and eventu-ally, the actual use of studying tactics sheds light on how studentsform and maintain their learning intentions throughout their study(Boekaerts & Corno, 2005).

A learning strategy is one that helps students to learn, remem-ber, and understand, in which the term ‘‘strategy” refers to variouscognitive operations that students use to accomplish their learninggoals (Garcia & Pintrich, 1994; Weinstein, 1988). Some learningstrategies are general in nature and can be used for a variety oflearning tasks. Others, instead, are more domain or task specific,but in many cases, the strategies are particularized versions of gen-eral strategies (Alexander, Graham, & Harris, 1998). Often, the taskand the environment provide guidelines as to what strategy tochoose, yet strategies are always generated by the person even ifthey are idiosyncratic or ineffective (Paris et al., 2001; Winne &Perry, 2000).

Weinstein and Mayer (1986) classified general learning strate-gies, such as rehearsal, elaboration, and organizational strategies,that are used to process and manipulate information into the de-sired form and to relate new information into existing informationstructures (Alexander et al., 1998). Rehearsal strategies involverepeating words in a correct serial order and highlighting or copy-ing the material to be learned. The main purpose of rehearsal strat-egies is to help keep information active in the working memoryand provide opportunities to process selected information further.Elaboration strategies involve paraphrasing or summarizing,explaining the ideas by making notes, and using prior knowledgeto make information more meaningful. The purpose of organiza-tional strategies is to translate information into another form(Weinstein, 1988). For example, constructing a concept map orga-nizes information in a hierarchical manner. A concept map visual-izes how concepts are hierarchically structured and how conceptsat different levels are related to each other. Moreover, conceptmapping requires relating new information to prior knowledge,and identifying and externalizing the state of knowledge and itsstructures (Hilbert & Renkl, 2008).

Previous studies have shown the advantage of using strategiesin learning (e.g., Pintrich & DeGroot, 1990). Studies have also de-scribed various conditions under which a strategy is effective (Ert-mer & Newby, 1996; Hadwin & Winne, 1996). For example,highlighting can be effective when the main ideas are selected,but only after the whole segment of text has been read or the seg-ment that has been highlighted is reviewed afterwards (for a moredetailed review, see Hadwin & Winne, 1996). Copying and pastingselections, which resemble highlighting in hypermedia learningenvironments, can be effective for learning, but only when stu-dents’ choices about how much to copy and paste are restricted(Igo, Bruning, & McCrudden, 2005). Slotte and Lonka (1999a)examined qualitative and quantitative value of spontaneous notetaking for text comprehension. The authors found that notes thathad been created by using generative strategies such as summariz-ing and writing in one’s own words improved learning, whereasnotes that were verbatim copies had little effect on learning. Also,a similar study that used concept maps revealed that merelyincluding relevant concepts on a map had little effect on the com-prehension of concepts, whereas the breadth and complexity ofconcept maps played a powerful role in students’ understanding

1036 J. Malmberg et al. / Computers in Human Behavior 26 (2010) 1034–1042

of the texts (Slotte & Lonka, 1999b). These studies, which wereconcerned with the effectiveness of a strategy, also showed (e.g.,Igo et al., 2005; Slotte & Lonka, 1999a, 1999b) that the effective-ness of a strategy depends on the way the strategy is eventuallyexecuted (Hadwin, Boutara, Knoetzke, & Thompson, 2004; Hadwin& Winne, 1996).

In the literature, learning strategies are not usually distin-guished from study tactics; rather, the terms are used as synonymsfor each other (Kirby, 1988; Winne, 2001). In this study, a distinc-tion between learning strategy and study tactic has been made.This distinction is considered important, because of the nature oflog file traces, which provides detailed information about thestudying actions, which are considered to be study tactics (Hadwinet al., 2007). Study tactics are defined as single cognitive operationsthat are used reactively. When studying tactics are used reactively,they do not involve a specific goal or active monitoring (Winne,2001; Zimmerman, 1998); rather, they involve only one step at atime, which refers to regulating and changing cognition on a needbasis by applying a technique such as underlining, note taking, orsummarizing (Winne, 2001). In contrast, a learning strategy is de-fined as a planned set of coordinated study tactics that are directedby a learning goal and aim to acquire a new skill or gain under-standing (Alexander et al., 1998; Weinstein, 1988; Winne, 2001;Zimmerman, 1998). In other words, the coordinated and strategicuse of study tactics involves a cyclical process of self-regulation.

2.1. Aim

The purpose of this study is to explore how elementary schoolstudents self-regulate their use of study tactics during a 5-weekscience project. This was done by capturing traces of elementaryschool students’ actions ‘‘on the fly” in the gStudy learning envi-ronment and analyzing the effectiveness of tactic use for learning.The specific research objectives are (a) to characterize what typesof study tactics are used in different situations in the gStudy learn-ing environment and how the use of these tactic types is patternedin these situations and (b) to investigate whether typical tactic usevaries among students, and if so, how it is linked to theirachievement.

3. Methods

3.1. Participants and context

The participants in the present study were 20 fourth-grade ele-mentary school students (12 boys and 8 girls), aged between 10and 11 years. The students participated in a 5-week science projectthat dealt with vital conditions of life. The pedagogical structure ofthe science project was designed to promote self-regulated learn-ing (Pintrich, 2000) and included classroom work with teachers,gStudy learning sessions, field work, and collaborative group work.

This study focuses on students learning within the gStudylearning environment. gStudy is an advanced multimedia learningenvironment in which contents, such as hyperlinked texts orgraphics, are displayed in learning kits (Hadwin et al., 2007; Ku-mar, Groeneboer, Chu, Jamieson-Noel, & Xin, 2006). gStudy alsoprovides a number of cognitive tools, designed based on prior re-search on self-regulated learning (Hadwin et al., 2007; Winneet al., 2006). These tools prompt students to deeply process thecontent of the learning kits and to practice study tactics and learn-ing strategies while they study (see Fig. 1).

In this study, students were instructed to use three differenttools: note, concept mapping and highlighting. With the note tool,students could create notes while reading the content presented intheir learning kits. The note tool also included different types of

templates that prompted students’ to ask questions and summa-rize or explain ideas in their own words. Using the concept maptool, students could construct concept maps by linking the notes,created with help of the note tool. Finally, with the highlight tool,students were able to highlight pieces of texts with different typesof readymade labels. The labels option were ‘‘I don’t understand,”‘‘important information,” and ‘‘interesting detail.”

3.2. Procedure

During the gStudy working sessions, the students were asked tostudy a science topic from the learning kit called ‘‘Vital conditionsof life”. The content of the kit was divided into six different subtop-ics, namely water, air, nutrition, heat, light, family, and humanrights.

The students regulated their study by choosing their study top-ics, their study duration, the starting point for studying, and espe-cially, the methods for using the three cognitive tools provided intheir learning kit. At the beginning of the science project, the stu-dents were instructed to use the note tool, concept map tool andhighlight tool while studying the content of the learning kit called‘‘Vital conditions of life”.

At the end of the science project, the students constructed amind map with paper and pen that reflected their general under-standing of the science topic. These mind maps were used as anindicator of the students’ learning and understanding of thesubject.

3.3. Measures

3.3.1. Log file tracesThe students’ studying activities in gStudy were recorded as log

file traces. Basically the studying activity consisted of two types oflearning events: students’ browsing of the contents in the learningkit (view events) and their use of cognitive tools (model events) forprocessing the information presented in the learning kit. In the logfile traces, the use of the three different cognitive tools was dividedinto more detailed information on how students’ actually usedthese tools. This resulted in the identification of nine differenttypes of tactics. In other words, each tactic type reflects a specificway to use the cognitive tools available. The logging system ingStudy recorded the order and duration of use of each tactic typeby the students, and also how students viewed the contents oftheir learning kits. This data were exported to an external XMLdatabase (Nesbit, Xhou, Xu, & Winne, 2007). Taken together, theseactivities composed a log file trace of each student’s tactic usewhen studying with gStudy. Log file traces from all the students’gStudy sessions (f = 172) were recorded and analyzed.

3.3.2. Mind mapsThe students’ mind maps were rated on four criteria, on a scale

of 0–4 points, so that the maximum number of points was 4. Thefirst two criteria were based on the identification of the main con-cepts and correct links between them. The last two criteria wererelated to the relevance and depth of clarification of the concepts.First, the mind maps were evaluated by two different researchers.If the ratings were unclear, a third researcher’s opinion was soughtto ensure inner congruence in the ratings. The purpose of this pro-cedure was to ensure that the categories in which the mind mapswere rated were consistent.

3.4. Data analyses

The analysis of this study was divided into two different parts.The first part of the analysis was conducted at the situational level,and focused on capturing the tactic types that were used in differ-

Fig. 1. gStudy learning environment.

J. Malmberg et al. / Computers in Human Behavior 26 (2010) 1034–1042 1037

ent gStudy sessions, classifying gStudy sessions based on the tacticuse, and capturing the patterned use of tactics in these gStudy ses-sions. In the second part, the level of the analysis was changed todeal with individual students. At the student level, a target wasto capture individual students’ typical approach for using tacticsacross gStudy sessions and illustrate how tactic use was linked tothe students’ achievements.

3.4.1. Situational level – Analysis of the gStudy learning sessionsAt the situational level, the analysis focused on the capturing

characteristics of tactic use from the log file traces. The analysisof log file traces consisted of two steps: data parsing and data min-ing. The purpose of data parsing was to identify the tactic typesthat students used while studying (Nesbit et al., 2007). In thisstudy, the tactic types that were parsed were as follows: creatinga new concept map, creating a note in a browser view, creating anote in a concept map, creating a new note in a note view, linkingnotes in a concept map, viewing the glossary, and three differenttypes of readymade labels (see Table 1). These tactic types reflectstudents’ learning activity within the gStudy learning environment(Nesbit et al., 2007; Zhou & Winne, 2009).

Table 1Cognitive tools available and tactic types they enable.

Cognitive tool (f = 3) Tactic types (f = 9)

Concept map tool Create new concept mapLink notes in concept map

Note tool Create note in a concept mapCreate note in a note viewCreate note in a browser

Highlight tool Label: Important informationLabel: Interesting detailLabel: I don’t understandView glossary*

* Marking indicates that there was no specific tool for performing that tactic type,since it was considered as a view event.

Once the tactics used by the students were identified from thelog file traces, data parsing yielded a time-stamped sequence oftactics, frequency of tactics, and occurrence of tactic types usedin each gStudy session as well as the mean duration of each tactic(see Fig. 2).

Next, an analysis was performed to classify each gStudy session.This classification was based on the activity and selection of differ-ent tactic types. The purpose was to identify situational differencesin the students approach for using study tactics. Since the studentswere capable of selecting the duration of study with gStudy, thisstudy captured the studying activity in each session. The activityin each session was measured by dividing the frequency of tactics,which varied between 2 and 42 (Mdn = 8 tactics per minute) withthe duration of the tactic, which varied between 3.1 and 45.6 min(Mdn = 13.6 min). The tactic types used across all gStudy sessionsvaried between two and nine (Mdn = 4). Classification of the gStu-dy sessions was used in K-means cluster analysis to identify similarapproaches for using tactics during. The cluster analysis resulted inthree different categories of gStudy sessions based on tactic use,which were frequent, moderate, and rare.

Finally, specific learning patterns for each category were ex-plored with the data mining technique to determine what andwhen study tactics were used in each category. Learning patternrefers to series of tactics that were used regularly in the same or-der. Learning patterns also illustrate how the tactic types are inter-twined within the frequent, moderate, and rare categories.

The frequency of the occurrence of the tactic types in these pat-terns was decoded. In this study, a series of tactics was consideredas a learning pattern if a sequence included at least three tactics.The data mining resulted from zero to thousands of learning pat-terns depending on the classification of frequent, moderate, or rare(see Fig. 3). In order to select representative learning patterns fromthe thousands of learning patterns that describe the common useof tactics in each category, the criteria for selection was set. Thecriteria was (a) the most frequent learning pattern (b) the longestlearning pattern. The longest learning pattern was selected to pro-

Fig. 2. Example of data parsing output.

Fig. 3. Example of data mining output.

1038 J. Malmberg et al. / Computers in Human Behavior 26 (2010) 1034–1042

vide a comprehensive picture of how the related and patterned tac-tic use actually was in addition to the most frequent tactic use.

3.4.2. Individual level – Analysis of student learningAfter the characteristics of the gStudy sessions had been exam-

ined, the analysis shifted to the student level. The change from thesituational level to the individual level helped to analyze whetherindividual students had a typical method of using tactics duringthe gStudy sessions. The students’ gStudy sessions varied from fiveto 12 (Mdn = 8). To determine each student’s typical method ofusing tactics, the mode of all the classified gStudy sessions was de-fined for each student.

Once a student’s typical approach to the task was identified, themind maps, which were created after the study period, were com-pared to examine how the students’ use of study tactics was linkedto the students’ level of understanding.

4. Results

The results of the analysis of the gStudy sessions (situational le-vel) are presented first, followed by the results of the analysis ofthe students’ learning (individual level). First, at the situational le-vel, the gStudy sessions were classified based on rare, moderate, or

frequent tactic use. Second, the frequency and occurrence of differ-ent tactic types and learning patterns that emerged in the threetypes of sessions are presented. Third, four examples of represen-tative learning patterns illustrate the differences in the use of tactictypes between frequent and moderate gStudy sessions.

Results from the individual students will reveal the most typicalapproach for using tactics by each student. The ratings from themind maps and the most typical tactic use (rare, moderate, or fre-quent) for each student are compared to illustrate the connectionbetween typical approach for using tactics use and the students’ le-vel of understanding of the subject.

4.1. Characteristics of study tactic use in different gStudy sessions

During the 5-week science project, the students logged intogStudy a total of 172 times. K-means cluster analysis was used toclassify the different gStudy sessions based on activity and theuse of different tactic types (see Table 2). The activity was chosenas a variable, because the time students spent with gStudy varied.In addition, students used maximum nine tactic types. The analysisresulted in three categories that varied from each other in terms ofactivity and the number of tactic types used (p = .00).

Next the three categories namely rare, moderate, and frequentwere defined in greater detail. These categories are different from

Table 2Classifying learning situations based on the tactic used in the gStudy learningenvironment.

Activity f Tactic types Tactics per minute

Mdn SD Mdn SD

Rare 45 3 .50 .36 .39Moderate 78 4 .50 .70 .39Frequent 49 6 .74 .99 .17

J. Malmberg et al. / Computers in Human Behavior 26 (2010) 1034–1042 1039

each others in terms of the activity and the use of different tactictypes.

The first category represents gStudy sessions during which thetactic use was rare (f = 45). The students used only three of ninetactic types (SD = .50) infrequently within each gStudy session(.36 tactics per minute).

The second category represents gStudy sessions during whichtactic use was moderate (f = 78). The students used four (SD = .50)of nine tactic types. This moderate tactic use was the most com-mon method of using tactics during gStudy sessions (.77 tacticsper minute).

The third category represents gStudy sessions during which thetactic use was the most frequent (f = 49). The students used six tac-tic types (SD = .74), and the students were constantly active as theystudied, since they employed almost one tactic per minute (.99 tac-tics per minute).

Next, tactic types and learning patterns identifiable in the threeclassified gStudy sessions were investigated.

In the gStudy sessions with rare tactic use, the tactic types werenot used in a patterned way. The activity was not focused on select-ing and modifying new information, because the tactic types thatwere available could be used for those purposes only (see Table 2).In the gStudy sessions with moderate use, five learning patternsemerged. Making a note in a concept map was part of each of thosefive learning patterns, but linking notes in a concept map occurredin only two learning patterns. This indicates that there were two ap-proaches for using tactics in a moderate way. The first approachconsisted of only note taking. The second approach to using tacticsin a moderate way focused on constructing concept maps.

In the gStudy sessions with frequent tactic use, 2374 differentlearning patterns emerged. Table 3 lists all the tactic types thatemerged in these learning patterns. The table also lists the fre-quency of learning patterns in which the particular tactic type oc-curred. The table includes also percentages to illustrate howtypical that tactic type actually was.

Table 3 presents how often a certain tactic type occurred inthese patterns. Labeling as ‘‘important information” was found in79% of the learning patterns and labeling as ‘‘interesting detail”was present in 78% of the learning patterns. Taking notes wasfound in 77% of the learning patterns. This indicates that the mostfrequent approach to use tactics involved first selecting segmentsfrom the texts (labeling) and then taking notes since these tactictypes occurred together in more than 56% of the observed learning

Table 3Tactic types and learning patterns that reflect frequent tactic use.

Tactic types Learning patterns(f)

Learning patterns(%)

Labeling: Important information 1871 79Labeling: Interesting detail 1842 78Make note in concept map 1818 77Linking notes in concept map 1268 53Labeling: I don’t understand 4 .16Make new concept map 2 .08Total number of learning

patterns2374

patterns. In addition, constructing a concept map (f = 1268) oc-curred in 53% of these learning patterns. Instead, labeling as ‘‘Idon’t understand” (f = 4) and starting over by creating a new con-cept map (f = 2) were not typically used tactic types in these learn-ing patterns.

4.2. Examples of representative learning patterns

The learning patterns in moderate and frequent gStudy sessionsreveal the tactic types actually selected by the students and howthey were used together.

To get a more detailed picture of how learning patterns werecomposed, examples of learning patterns are presented in a graph-ical form (see Fig. 4). On the X-axis, the order of different tacticsand the length of the learning pattern are presented, whereas onthe Y-axis, the tactic types that occurred are presented with thehelp of symbols. The selected learning patterns are the longestand the most frequently used and that describe frequent and mod-erate tactic use.

The most frequent learning pattern was exactly the same be-tween the frequent (A) and moderate (B) gStudy session basedon the tactic use. The pattern aimed at constructing a conceptmap by making two notes and then linking them together.

The longest learning pattern (C) among frequent gStudy ses-sions based on the tactic use was used 11 times and constituted12 tactics of four different tactic types. This specific learning pat-tern began with labeling text as ‘‘important information” and‘‘interesting information”. Labeling was used nine times succes-sively. After students selected pieces of information by labeling,the subsequent tactic use concentrated on taking two notes on aconcept map and forming one link between them.

The longest learning pattern (D) among moderate gStudy ses-sions based on the tactic use was used 20 times and constituted aset of four tactics. This tactic use was totally focused on note taking.

According to the learning patterns, the frequent gStudy sessionsbased on the tactic use involved extensive use of labeling, followedby note taking and constructing a concept map. That is, frequenttactic use usually involved selecting a lot of information to processby using four different tactic types. In contrast, the moderate gStu-dy sessions based on the tactic use involved only note taking andthen linking the notes together. That is, moderate tactic use usuallyfocused on constructing concept map. In sum, the occurrence oftactic types and the frequency of learning patterns varied amongthe three classified gStudy sessions.

To investigate whether the typical tactic use varied among stu-dents, the students’ most typical approach to tasks was identifiedby defining each student’s mode of tactic use in each gStudy ses-sion (see Table 4). In addition to the three categories of gStudy ses-sions based on the tactic use (frequent, moderate, or rare), onemore category was added: where the students had no mode. Whenthe students had no mode, the tactic use varied even between rareand moderate (n = 2) or rare, moderate, and frequent (n = 2).

Moderate use of tactics was the most common studying methodamong students (n = 8). There were three students whose typicalmethod of using tactics was frequent. For three other students, thetypical method of using tactics was rare, and four students had notypical method of using tactics. Even among the students who hada typical method of employing tactics, there were no students whosetactic use remained the same across all gStudy sessions.

Next, the students’ typical approaches and the students’ ratingsbased on their mind maps were compared to determine how tacticuse is related to the students’ level of understanding of the studiedsubject (see Table 4). Mind map scores, which were used as anindicator of the students’ level of learning, varied from the lowestscore of 2 (f = 6) to the highest score of 4 (f = 5), and 3 (f = 9) wasthe most common score.

Table 4Contrasting mind map scores to the mode of individual students’ gStudy sessions.

Individual students’ tactic use Mind map scoresTotal 2 3 4

Rare 5 1 4Moderate 8 1 6 1Frequent 3 2 1No mode* 4 3 1Total 20 6 9 5

* Four students who had two or three typical approaches.

Fig. 4. Examples of learning patterns that describe frequent and moderate tactic use.

1040 J. Malmberg et al. / Computers in Human Behavior 26 (2010) 1034–1042

These mind map scores contrasted with the individual students’typical manner of using tactics across classified gStudy sessions,show how tactic use is linked to the students’ learning and under-standing of the subject that had been studied. Students whose typi-cal method of employing tactics was rare scored highest on theirmind maps except one student who had three points. The studentswhose typical method of employing tactics was moderate scoredaverage on their maps. Six students out of eight scored three pointson their mind maps, one student scored four, and one student scoredtwo points. The students whose typical method of employing tacticswas frequent scored the lowest on their mind maps. Two studentsscored two points, and one student scored three points on the mindmap. Also, the students who had no typical approach to the tasksscored the lowest on their mind maps. Three students scored twoon their mind maps, and one student scored three points.

5. Conclusions

The results revealed that across the gStudy learning sessions thestudents’ use of different study tactics varied from frequent to rare.Moreover, this study showed the different tactics that were typi-cally used by students and how their use of tactics was related totheir achievements. However, when the tactic use was frequent,

it did not lead to deep learning. Moderate tactic use was quiteeffective for learning, but still, rare tactic use led to deep learningand understanding of the subject that had been studied. The eightstudents who typically used moderate study tactics achieved bet-ter scores than the students who had no typical approach to tasksor whose typical method of using tactics was frequent. Yet, theydid not score better than the students whose typical method ofusing study tactics was rare.

In the gStudy sessions where the tactic use was rare, the activitywas not focused on selecting new information. Also, the studentswhose tactic use was more often rare learned the most about thesubject that had been studied. However, the log file traces didnot reveal if the students read the information texts in the sciencekits more closely, or modified already existing information, or reor-ganized their concept maps, which has been found to contributedeep learning (Pintrich & DeGroot, 1990).

In gStudy sessions, where the tactic use was frequent, the stu-dents’ activity focused on selecting and modifying new informa-tion by using multiple study tactics. In those situations, thestudents experimented with different study tactics, but the stu-dents might not have engaged in evaluative processes about whatinformation to select (Igo et al., 2005). This suggests that the stu-dents who used study tactics frequently did not have specific goalsfor learning, so they used the study tactics reactively (Zimmerman& Risemberg, 1997).

In the gStudy sessions where tactic use was moderate, the ap-proach for using tactics was focused on taking notes and con-structing a concept map. In those gStudy sessions the selectionof information was tight when compared to the gStudy sessionswith frequent tactic use. That is, the process of taking only afew notes improved learning when compared to labeling and thentaking notes, which characterizes frequent approach for usingtactics.

The study results did not reveal that the use of many study tac-tics improves learning results (Hadwin et al. 2007). In fact, the stu-

J. Malmberg et al. / Computers in Human Behavior 26 (2010) 1034–1042 1041

dents whose approach for using tactics was more often rarelearned the most. Earlier research findings show that novice learn-ers, such as children, do not usually employ studying tactics in astrategic manner; rather, they are more reactive with their use oftactics. They do not necessarily have specific goals for learning,and mostly they do not spend time on planning, which requires aslow rather than a reactive approach (Zimmerman & Risemberg,1997). It seems that the distinguishing feature in strategic learningis not in the tactic use itself, but rather in the way that tactic use isactivated (e.g., Kiewra, Benton, Kim, Risch, & Christensen, 1995;Slotte & Lonka, 1999a, 1999b). Activating tactics requires monitor-ing the effectiveness of a tactic use and choosing the studying tac-tic because of the need, and not because of the possibility. Despitethe contextual guidelines about what study tactics to use, differentstudy tactics are not necessarily effective unless their meaning forlearning is invented by the student.

Since these study results are speculative in terms of rare tactic useand the quality of tactic use cannot be proved by the data, more de-tailed analysis is needed. Qualitative analysis about what informa-tion the students actually selected, what kind of notes they tookand what kind of concept maps they constructed when studyingwould provide additional information about what makes the tacticuse strategic in practice. Since strategy use is goal directed (Linnen-brink & Pintrich, 2001), investigating how situational goals are re-lated to students’ tactic use would reveal more about how tacticuse becomes strategic.

In this study, the students’ learning outcome was measured byanalyzing the mind maps created by the students after the 5-weekstudy period. A mind map was selected to measure learning out-comes, since it externalizes the current state of knowledge ratherthan replicates facts or concepts learned by heart (Hilbert & Renkl,2008). Also, it has been acknowledged that the students who havehigh self-regulated learning skills tend to perform better on theirmind maps than the students with weaker self-regulated learningskills (Lim, Lee, & Grabowski, 2009). Mind maps have a dual effectwhen considering self-regulated learning – they foster the use ofgenerative strategies, and the students who use more generativestrategies tend to have high self-regulation skills.

One objective of this study was to overcome the limitations ofthe earlier methods in research about self-regulated learning. Thelog file traces generated detailed information on twenty students’use of study tactics in different learning situations when studyingscience with gStudy. Log file traces helped to gain informationabout students’ actual tactic use within different learning situa-tions instead of creating profiles of students’ typical approachesthat rely on self-report data. Because of this advantage, log filetraces are very promising for understanding not only self-regula-tion but also other aspects, such as the process of collaboration(Gress, Fior, Hadwin, & Winne, in press). Yet, it is challenging tointerpret log file traces and locate those tactic types that revealmeaningful learning activity. By gathering log file traces from theenvironments such as gStudy, it is possible to create a detailedanalysis of the students’ learning process. Log file traces show,for example how the student views the learning content, whenthe student returns back to the challenging text segment, orwhether the student passes through without attempts to self-reg-ulate learning. Log file traces could be complemented with otherprocess oriented data, such as experience sampling methods in or-der to learn more about the reasons why some students engage in acertain activity instead of other (Järvelä & Volet, 2004). Situationspecific and process oriented methods have the potential to furtherour understanding of the key processes that the students need toregulate in order to learn effectively in variety of learning settings(Volet & Järvelä, 2001).

The practical value of this study is apparent when consideringthe opportunities and possibilities of hypermedia-based learning.

Students use different tools available on the Internet when theystudy, but there is a lack of research on how students actually se-lect and use study tactics while studying and when the tactic useactually has an impact on learning. Several practical tools are avail-able on the Internet that students can use while studying. Forexample, copy and paste selection is a popular tactic, but thereare also more sophisticated tools such as concept mapping andbookmarks. Understanding tactic use in more detail will advance,for example, pedagogical orchestration of computer supported col-laborative learning and instructional design of future learningenvironments (Dillenbourg, Järvelä, & Fischer, 2009).

Acknowledgements

This research was supported by Grant no.1110734 from theCouncil of Cultural and Social Science Research, Academy ofFinland.

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