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Motivational strategies of students in Virtual University · Motivational strategies of students in...
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Motivational strategies of students in Virtual University
Anne Nevgi Department of Education
University of Helsinki A paper to be presented in Evidence-Based Policies and Indicator Systems Third International Inter-disciplinary conference 4.-7.July 2001, University of Durham,
England
Introduction This paper discusses the role of virtual university students' motivational strategies for
learning. The paper is based on a study, which is part of a larger IQ-FORM research
project (WWW-site http://www.edu.helsinki.fi/iqform/). First is presented the aim and
the theoretical background of IQ FORM research project and then is introduced
theoretical framework for motivational strategies. In order to validate and develop the
measurement tool for Motivational learning strategies of Virtual Unversity students’
the pilot study was taken in January 2001. The data of pilot study and the validation
procedures of factorial structure of the measurement are presented and the effect of
tutorial guidance to the students’ motivational strategies is demonstrated.
IQ FORM research project IQ FORM research project began at the end of 1999 and it is a co-operative project
with educational, computing and information technology sciences involved. The aim
of the project is to develop tools for virtual university students to help them become
more effective learners of virtual university.
The main tasks of the project:
• The project will explore how different learners study in virtual environments
and what kind of support they would need.
• The project has been developing a flexible IQ FORM data bank, which will be
used during 2001 in virtual university courses.
• The project is also developing a tutorial package, which can be adapted
according to the needs of specific courses, students and teachers. (IQ-FORM
research team, http://www.edu.helsinki.fi/iqform/)
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The Project Director is Professor Hannele Niemi, Dean of the Faculty of Education at
the University of Helsinki. The researchers of the IQ-FORM project are mainly from
University of Helsinki but they have co-operation and networking partners in
universities of Oulu, Tampere and Joensuu, which will apply new learning and
tutoring tools in their virtual courses.
The theoretical framework behind IQ-FORM is based on theories of mediated
learning, distributed cognition, and on Gardner’s multiple intelligence theory
(Gardner 1983) and Pintrich & Ruohotie’s motivational theory (Ruohotie & Pintrich
2000). A social navigation in virtual learning environments is discussed as an
empowerment of student learning (Niemi 2001).
IQ-FORM tools for learners
The main tool to help the learner is called IQ-FORM (intelligent questionnaire), an
interactive databank. IQ-FORM gives information about the qualities of students as
learners e.g. learning profiles and motivational structures as social navigation during
their virtual studies (Niemi 2000a). The questionnaires work as a data bank, from
which students may select different combination to become more conscious of their
learning styles and motivational strategies and changes in these qualities during the
course (IQ-Research group 2001a and IQ-Research group 2001b). The profiles, which
are based on the tests, tutor the students to find help and support from their teachers or
peers and to encourage them to use new kind of learning material or routes to find
more effective learning strategies.
The other tool, or set of tools consists of a tutoring package for learners and teachers.
This package contains guidance packages for students, student groups (in specific
virtual classes) and teachers. The student packages will lead the student for example
to analyze their learning styles, strengths and weaknesses and help them, when a
problem situation occurs.
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Motivational strategies for learning in IQ-FORM In this paper the focus is on the motivational strategies of students and a theoretical
background is based on a theory presented by Pintrich and Ruohotie (2000. The main
task is to develop a tool to help Virtual University students to identify their
motivational strategies and to improve their learning skills and strategies. The theory
of motivational strategies for learning developed by Pintrich and Ruohotie (2000) is
based on general social cognitive view of motivation, cognitions and metacognitions,
and self-regulated learning. The motivational constructs are interpreted rather as
social cognitive than as a need or a drive. The cognitive and metacognitive
components are seen from a general information processing perspective, not from a
structural and developmental view of cognition. (Pintrich & Mckeachie 2000.)
Motivated strategies of learning can be measured as an aptitude by self-ratings or as
an event observed by outsider rater (Winne & Perry 2000). Paul Pintrich (Pintrich,
Smith, Carcia, and McKeachie 1993) has developed Motivated Strategies for
Learning Questionnaire (MSLQ) to assess students’ motivation and cognition in the
classroom. MSLQ is based on students’ self-ratings. Pekka Ruohotie (1999) has
further developed the questionnaire in Finnish context of vocational teacher
education. In IQ-FORM research project the measurement of Motivational Strategies
for Learning Intelligent Questionnaire (MSLIQ) was adapted from the original MSLQ
by Pintrich (1993) and from the Finnish version adapted by Pekka Ruohotie (2000).
The first version of MSLIQ consisted of two parts: A) Learning Experiences and
Motivation and B) Cognitive and Metacognitive Learning Strategies.
Method The research project of motivational strategies is a part project of IQ-FORM research
project and it proceeds in the following phases: 1) pilot study – survey in order to test
the measurement of motivational strategies in learning, 2) validation and creation of a
MSLIQ tool for virtual university courses, 3) testing and retesting the MSLIQ tool in
real virtual university courses and with surveys. The research is thus action research,
where the aim is at the same time to develop and improve the virtual learning
environment to support more effective learning, and to gain new knowledge about
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motivational strategies students use in their learning. At the present paper the first
results of the pilot study are discussed. Pilot study
In the IQ-FORM research project a pilot study was taken in January 2001 to test the
measurements based on the theory of motivational strategies in learning (Pintrich &
Ruohotie 2000) and on Gardner’s multiple intelligence theory (Gardner 1983). The
data for pilot research was collected by a questionnaire from the undergraduate
students (N = 256) of five Finnish universities. In this study the aim is to test and
develop the measurement of motivational strategies for learning in order to create a
tool for IQ-FORM databank, which can be used by virtual university students and
their tutors. The research questions are: What are the motivational, metacognitive and
cognitive strategies for learning? What kind of differences can be found in
motivational, cognitive and metacognitive strategies for learning when compared the
groups by sex, age, main subject, or the tutorial guidance?
In order to develop the measurement of motivational strategies for learning the
confirmatory factor analysis procedures were run to validate the factorial structure of
MSLIQ. The influence of demographic data was analyzed by ANOVA.
Respondents were mostly young adult students (mean age 23,1; youngest 17 and
oldest 50), equally males (127, 50,2 %) and females (126, 49,8 %). They represented
five different majors (Humanities and Art, Social and Behavioral Sciences, Teacher
Education, Technology and Science, and Agriculture and Forest) from five different
universities (University of Helsinki, University of Joensuu, University of Tampere,
University of Oulu, and Helsinki University of Technology). Most students had
passed their matriculation exam and had began their studies in the University during
the years 1998 to 2000. Respondents were most commonly students of a first or a
second year, approximately 60 % of them had 20 to 60 study credits (for master
degree the demand is 160 study credits). Most of the students reported to have good
study motivation, and they were satisfied to their major. Most of the students (60 %)
told also that they had proceeded well in their studies. The demographic background
of students’ is presented in details in the Appendix 1.
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Validation of factorial structure of motivational scale
There were 26 items (a01 to a26) measuring learning experiences and motivation (part
A) and 40 items (b01 to b40) measuring the cognitive and metacognitive learning
strategies (part B). The scale runs from 1 (disagree strongly) to 5 (agree strongly).
Table 1 indicates the area of motivational strategies covered by questionnaire used in
the research project. In the table 1 is also presented the new model of measurement. First the motivational scale (Part A) was examined (items a01 to a26) and a forced
five-factor solution was used for theoretically acceptable results. The items for
separate factor analysis were selected based on theoretical analysis of measurement
(see table 1).
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Table 1. Theoretical and factorial structure of motivational strategies for learning
(adapted from Ruohotie & Nokelainen 2000, 163)
Target of measurement
Theoretical analysis of measurement instrument
Factorial structure A new model of the measurement
Value components • Intrinsic Goal Orientation • Task Value of Learning
Meaningfulness of Studies (fa3) Interest and Task value of Learning (fa3a) Utility of Studies (fa3b)
Expectancy components • Intrinsic Control Beliefs • Extrinsic Control Beliefs • Self-Efficacy • Expectancy for Success
Expectancy for Success (fa1) Self-Efficacy (fa4)
Part A Motivational scale
Affective components • Test Anxiety and Self-
Worth
Test Anxiety and Nervousness in a Test Situation (fa2)
Part A Motivational scale
Resource Management Strategies • Time and Study
Environment Management Strategy
• Effort Regulation Strategy
• Peer Learning Strategy • Help Seeking Strategy
Time Management strategy (fb1) Effort Regulation (fb2) Self-Regulation of Learning (fb2a) Persistence in Studies (fb2b) Help Seeking and Peer Learning strategy (fb4)
Part B Resource Management scale
Part B Cognitive scale
Metacognitive Strategies • Planning Activities • Monitoring Activities • Regulating Strategy Cognitive Strategies • Rehearsal Strategy • Elaboration Strategy • Organization Strategy • Critical Thinking Strategy
Rehearsal Strategy (fc1) Critical Thinking (fc2) Focus on Essential in Learning (fc3) Constructive Learning (fc4) Using Keywords (fc5) Application of Theory (fc6) Reflection on Learned (fc7)
Part C Cognitive scale
A forced four-factor solution and free solution (PCA) were used for comparison and
further examination of factorial structure. The five-factor solution explained total
variance 49,0 % (see table 2). Each of the five factors was tested with separate factor
analysis (method Maximum Likelihood with Varimax rotation), and the reliability of
the factors was tested with Cronbach’s alpha (see table 3).
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Table 2. A five-factor solution of the motivational scale.
Factors Variables
1 2 3 4 5
A01 ,489
A02 ,437 ,301 ,388
A03 ,314
A04 ,853
A05 ,795
A06 ,541 ,298
A07 ,449
A08
,313
A09
,626
A10 ,392 ,493
A11 ,663
A12 -,124 ,859
A13 -,404
A14 ,634
A15 ,601 ,357
A16 ,437 ,537
A17 ,559 ,307
A18 ,444 ,387
A19
,891
A20 ,695
A21 ,257 ,568
A22 ,912
A23 ,611
A24
,712
A26 ,554
Extraction Method: Maximum Likelihood. Rotation Method: Varimax with Kaiser Normalization. Rotation converged in 7 iterations.
Note: Values less than +/-.30 are suppressed.
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The expectation for success factor consisted of two dimensions: the will and the
expectation for good grades, and the belief in own ability to learn and success in
studies. Examination of factor showed that the variable A07 differed according to it’s
meaning from other variables (A07 “I want to obtain the best possible grades”) and it
was excluded from factor.
The anxiety and nervousness in test situation factor differentiated into two
dimensions: a) anxiousness due to a comparison of own performance with the peers'
performance, and b) nervousness during the test situation.
In original four-factor model the meaningfulness of studies consisted of intrinsic and
extrinsic motivation. According to the model the intrinsic and extrinsic motivation can
be described as one dimension, where a person is both intrinsically and extrinsically
motivated to study. The examination of factor showed that the meaningfulness of
studies factor could be divided into two different dimensions: a) Interest and Task
Value of Studies, and b) Utility Value of Studies.
The Self-Efficacy factor could be separated into two different dimensions: a) Self-
efficacy as a belief in own ability, and b) Self-efficacy as a belief in own effort. The
variables for the factor were selected to describe self-efficacy as a belief in own
effort. The factor will thus stress the meaning of intrinsic control beliefs.
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Table 3. Separate factor analysis of the five-factor solution for the motivational scale
Factor GFI for original
factor Alpha for original factor
GFI for modified one-factor model
Alpha for modified one-factor model
Expectation for Success (fa1) A05, A07, A11, A15, A20
37.6*** df 5
.78 5.10 n.s. df 2
.83
Anxiety and nervousness (fa3) A03, A08, A12, A14, A19
37.3*** df 5
.75 1.8 n.s df 2
.76
Meaningfulness of studies (Original) A01, A16, A17, A21, A22, A23, A26
368.9*** df 35
- - -
Interest and task value of studies A01, A16, A17, A21, A23, A26
25.6** df 9
.79 0.4 n.s. df 2
.73
Utility value of studies A04, A10, A13, A22
35.3 *** df 2
.77 - -
Self-Efficacy (effort) A02, A06, A09, A18, A24
81.4*** df 4
.62 62.6*** df 1
.64
Self-efficacy (ability) A02, A06, A18, A24
- - 11.2 ** df 2
.63
Extraction: Maximum Likelihood, Rotation: Varimax
Validation of factorial structure of cognitive and metacognitive scale
Second part of questionnaire (Part B) consisted of 40 items measuring the resource
management, and metacognitive and cognitive strategies of learning. First the
explorative factor analysis was run, and then the forced solution of a five-factor model
was examined. It consisted of five factors: Critical Thinking skills, Learning and
Note-taking skills, Effort regulation skills, Help Seeking Strategy and Time and Study
Environment Management Skills. The solution explained 37,4 % of variance (see
table 4). The result was compared with Kautto-Koivula study (1999) and Ruohotie
and Nokelainen (2000) analysis.
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Table 4. The five-factor solution of cognitive and metacognitive strategies in learning
Factor Variables
1 2 3 4 5
B01 ,604
B02 -,544
B03 ,404
B04 ,425
B05
,475
,655
B06 -,566
B07 ,533 ,314
B08 ,385
B09
-,493
B10 ,393
B11 ,529
B12 ,570
B13 ,378 ,351
B14
,659
B15 ,332 ,310
B16 ,615
B17 ,516
B18 ,503
B19 ,363 ,586
B20 ,672
Table continues.
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Table continues.
B21 ,501
B22 ,420 ,414
B23 ,586
B24 ,548 ,436
B25
B26 ,370
B27 -,540
B28 ,413
,119
B29 -,655
B30 ,497
B31 ,615 ,299
B32 ,542
B33 ,526
B34 ,427
B35 ,633
B36 ,617
B37 ,426
B38 ,807
B39 ,323
B40
Extraction Method: Maximum Likelihood. Rotation Method: Varimax with Kaiser Normalization. a Rotation converged in 8 iterations.
Note: Values less +/- .30 are suppressed.
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The further theoretical and confirmatory factor analysis of the structure of
measurement lead to the division of the measurement, which was divided into two
different parts: a) Resource Management Strategies (RMS) and b) Cognitive
Strategies in Learning (CSL). The items VB01, N02, B03, B05, B06, B09, B12, B13,
B14, B17, B18, B19, B25, B26, B27, B29, B36, B38, B39, and B40 were included
into RMS measurement (see new part B in table 1). The items B04, B07, B08, B10,
B11, B15, B16, B20, B21, B22, B23, B24, B28, B30, B31, B32, B33, B34, B35, and
B37 were included into CSL scale (see new part C in table 1).
The new factorial structure of RMS was tested first with an explorative factor analysis
(Maximum likelihood) and a free solution (PCA). The model of four factor solution
was selected and tested with forced Maximum Likelihood factor analysis. The result
of analysis supported the new model. Explanation of variance was 39.1 %. Each of
the four factors was tested with a separate factor analysis (method Maximum
Likelihood with Varimax rotation) and with reliability analysis (Cronbach's alpha)
(see table 5).
The new measurement of the CSL was first tested with forced a seven factor model
(Maximum Likelihood) (see table 6) and with a free solution (PCA). The model
explained 47,8 % of variance. The result showed that the model was approvable, but
needs to be modified and tested again. The rotated factor matrix is presented in the
table 6. The reliability of a seven factors’ theoretical model was tested with
Cronbachs’ alpha (see table 7).
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Table 5. Separate factor analysis of the four-factor solution for the resource
management scale
Factor GFI for one-
factor model Alpha for one-factor model
GFI for modified one-factor model
Alpha for modified one-factor model
Time Management (fb1) B01, B05, B12, B27
11.9 ** df 2
.71 -
-
Effort regulation (fb2) B02, B06, B13, B17, B18, B25, B26, B29, B36, B39
95.2 *** df 35
.77 - -
Self-Regulation of Learning (fb2a) B13, B18, B25, B26, B39
9.8 n.s. df 5
.63 - -
Persistence in Studies (fb2b) B02, B06, B17, B29, B36
6.6 n.s. df 5
.75 - -
Help Seeking and Peer Learning Strategy (fb4) B03, B09, B14, B19, B38, B40
38,2 *** df 9
.46 1.1 n.s df 2
.74
Method: Maximum Likelihood with Varimax rotation.
Time and study management factor included two components: a) conscientiousness in
keeping time, and b) effective use of time. There are different levels in time
management varying from monthly and weekly scheduling to the effective use of
current hour for studying. The factor did not describe the management of study
environment as in the original model.
In the Effort Regulation Strategy was distinguished two different aspects of
regulation: a) Self-Regulation in Learning and b) Persistence in Studies. Effort
regulation can be described as a student’s general self-management in terms of effort
and persistence. The further examination of two effort regulation dimensions showed,
that it was theoretically appropriate to divide effort regulation into two different
factors. The self-regulation in learning describes students’ ability to control and
monitor their own learning process and change the amount of effort demanded for
different study tasks. The persistence in learning is seen as a foresight and strength to
continue studies during the difficulties and / or dull study tasks. A persistent student
looks for the final goal of studies and can therefore stand for the inconvenience of a
current study period.
Peer Learning and Help-Seeking factor was validated as a student’s social ability to
ask help from peers in his/her study problems. This factor resembles Sternberg’s
(1985) notion of practical intelligence. A good student knows when he/she needs help
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and can also identify the person/s from whom to ask help. From the factor was
excluded variable B40 “I try to get feedback about my performance from my
teachers”, and so the factor emphasizes especially help seeking from peers.
Table 6. The seven-factor solution of cognitive learning strategies (20 items).
Factor 1 2 3 4 5 6 7
B04 ,413 ,196 ,233 ,134
B07 ,323 ,197 ,738 ,229 ,116 ,154
B08 ,550 ,367
B10 ,140 ,292 ,180 ,420
B11 ,593 ,308 ,120 ,355
B15 ,267 ,599
B16 ,400 ,131 ,530
B20 ,709 ,142 ,129 ,180
B21 ,468 ,297 ,300 ,191
B22 ,307 ,461 ,118 ,169 ,196 ,156 ,313
B23 ,152 ,183 ,126 ,295 ,507 ,193
B24 ,389 ,385 ,365 ,241 ,261
B28 ,340 ,296 ,251
B30 ,216 ,146 ,140 ,126 ,663
B31 ,570 ,167 ,211 ,279 ,105 -,116
B32 ,164 ,152 ,169 ,453 ,270 ,372
B33 ,201 ,142 ,699
B34 ,477 ,208 ,142 ,199 ,100
B35 ,468 ,154 ,110 ,383
B37 ,120 ,122 ,589 ,197 ,139
Extraction Method: Maximum Likelihood. Rotation Method: Varimax with Kaiser Normalization. a Rotation converged in 13 iterations.
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Table 7. The Seven-factor solution for cognitive strategies (20 items)
Factor Items Alpha
Rehearsal Strategy B08, B37 .42
Critical Thinking Strategy B07, B16, B33 .65
Focus on the Essential
Strategy
B11, B30 .53
Constructive Thinking
Strategy
B15, B22 .60
Use of Keywords Strategy B23, B28, B32 .60
Application of Theory
Strategy
B21, B24, B34, B35 .71
Reflection on Learned
Things Strategy
B04, B10, B20, B31 .64 (B10 excluded)
.60 (B10 included)
Rehearsal strategy of learning is outlined as a basic cognitive strategy with
elaboration, and organizational strategies of learning. Each of these strategies also has
a basic and a complex version, depending on the nature of a learning task (Pintrich &
McKeachie 2000, 41.) Here the rehearsal strategy describes more a complex than a
basic strategy. For the next phase in the research project the items belonging to this
factor were reformed in order to stress the meaning of more deep rehearsal learning
strategy.
Critical thinking strategy is described as a students’ ability to solve problems, make
critical evaluations, and comparisons. There is done plenty of research on this area,
and also in the new virtual learning environment and distance education area (eg.
Bullen 1998). The factor was found reliable (alpha .65).
The Focus on Essential strategy describes how students’ concentrate and find out the
essential and central ideas in their learning material. A good student is not only taking
notes of all the material she / he studies, but also organizes learning material to more
important and less important areas.
Constructive Thinking strategy means that a student is able to combine a new
knowledge with his / her previous knowledge and construct the meaning of studied
subjects.
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Use of Keywords strategy resembles close the previous focus on essential strategy. A
student makes notes of the central concepts and ideas, classifies them. These
keywords can be used during rehearsal period of the study process, and thus the
rehearsal becomes more complex and leads to more deep understanding in learning.
Application of Theory strategy can described as a students’ application of learned
theories, new things, into different everyday situations, or as a comparison of theories.
The transfer of learning resembles this factor.
Reflection on Learned strategy is used, when a student concentrates to think over the
learned things and makes questions, creates analogies, or explains the things he / she
has learned. This strategy can be described also as an elaboration strategy.
The effect of tutorial guidance to students’ motivational and
cognitive strategies in learning
The main task of the research project (IQ-FORM) is to develop a tool for students,
and their teachers or tutors, in order to improve students’ learning strategies. The tool
will work like an intelligent electronic helper or tutor for a student, and help him/her
to become more aware of his/her motivational strategies in learning. The aim is, that a
student can with the help of IQ-FORM-tool develop his/her own study planning and
management skills.
For the evidence of the meaningfulness of the new factorial structures of MSLIQ the
ANOVA procedures were run to examine the connections between the three different
scales (MS, RMS, CSL) of MSLIQ and received tutorial guidance. The students who
had received tutorial help during there studies were selected from data (N = 182).
Students were classified into three groups according to the amount of tutorial help
they had received during their studies. Students who had got plenty tutorial help
(N=12, 3 males and 9 females) were only few, but for a more detailed analysis they
were kept as a one group. Students with some tutorial help (N = 69, 46 % males and
54 % females) were classified as a group and so were also students with a minimum
tutorial help (N = 101, 53 % males and 47 % females). Students with more tutorial
help came from universities of Helsinki and Joensuu and students with less tutorial
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help were from universities of Oulu and Tampere. There were found no differences in
receiving tutorial help according to the age.
Table 8. Group: Tutorial help. Mean Scores, standard deviations (in parentheses) and F-values with significance on factors of motivational strategies. Factor Groups: Amount of a tutorial help during the studies
Plenty
N = 12
Some
N = 69
Minimum
N = 101
All
N = 182
F (sig)
Expectation of Success
4.00 (0.36)
3.76 (0.54)
3.49 (0.75)
3.62 (0.67)
5.5 **
Anxiety and Nervousness in a test situation
2.23 (0.88)
2.37 (0.83)
2.43 (0.89)
2.39 (0.86)
0.33 n.s.
Interest and Task Value of Studies
3.88 (0.87)
3.85 (0.54)
3.61 (0.71)
3.72 (0.67)
2.94 p<.055
Utility Value of Studies
4.17 (0.47)
4.15 (0.59)
3.93 (0.71)
3.72 (0.67)
2.53 p>.082
Self-Efficacy (effort)
3.71 (0.46)
3.87 (0.58)
3.96 (0.56)
3.91 (0.56)
1.31 n.s.
Students who had got more tutorial guidance reported that their belief in success in
their studies is higher compared with students with less or no tutorial guidance.
Students with more tutorial guidance had also higher interest for their studies and they
were more motivated for utility values in studies. These latter differences are not
statistically significant. Females, who had received tutorial help, were more motivated
of the interest and task value of the studies than males (F = 7.04, p<.01). Females and
males had no differences in motivational strategies in no-tutorial guidance group.
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Table 9. Group: Tutorial help. Mean Scores, standard deviations (in parentheses) and
F-values with significance on factors of resource management strategies.
Factor Groups: Amount of a tutorial help during the studies
Plenty
N = 12
Some
N = 69
Minimum
N = 101
All
N = 182
F (sig)
Time Management Strategy
3.00 (0.89)
3.12 (0.83)
2.63 (0.76)
2.84 (0.83)
8.04 ***
Effort Regulation
3.27 (0.62)
3.32 (0.52)
3.15 (0.69)
3.22 (0.63)
1.58 n.s.
Persistence in Studies
3.54 (0.58)
3.42 (0.73)
3.14 (0.77)
3.27 (0.76)
3.64 *
Peer Learning and Help Seeking Strategy
3.08 (0.89)
3.50 (0.61)
3.36 (0.81)
3.40 (0.75)
1.84 n.s.
Students with more tutorial help were more effective in their time management
strategies and were also more persistent in their studies compared with the students
with less tutorial guidance. There were no significant differences between males and
females in resource management strategies for learning, and the groups with tutorial
guidance and no-tutorial guidance did not also differ.
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Table 10. Group: Tutorial help. Mean Scores, standard deviations (in parentheses)
and F-values with significance on factors of cognitive strategies.
Factor Groups: Amount of a tutorial help during the studies
Plenty
N = 12
Some
N = 69
Minimum
N = 101
All
N = 182
F (sig)
Rehearsal Strategy
3.04 (0.84)
3.20 (0.70)
3.01 (0.75)
3.08 (0.74)
1.44 n.s.
Critical Thinking Strategy
3.14 (0.85)
3.03 (0.77)
2.78 (0.79)
2.90 (0.80)
2.59 p<.08
Focus on the Essential Strategy
3.79 (0.75)
3.93 (0.58)
3.53 (0.77)
3.70 (0.73)
6.82 ***
Constructive Thinking Strategy
4.46 (0.62)
4.08 (0.66)
3.90 (0.66)
4.00 (0.67)
4.69 **
Use of Keywords Strategy
3.47 (1.08)
3.63 (0.71)
3.20 (0.85)
3.38 (0.85)
5.86 **
Application of Theory Strategy
3.67 (0.73)
3.58 (0.61)
3.35 (0.73)
3.46 (0.70)
2.81 p<.06
Reflection on Learned Things Strategy
3.08 (0.83)
3.07 (0.66)
2.82 (0.71)
2.93 (0.70)
2.9 p<.06
Students with more tutorial guidance used more effective cognitive learning strategies
compared with students with less tutorial guidance. Females used more focusing on
essential (F = 6.32, p< .01), constructive thinking in learning (F = 8.70, p<.01) and
keywords (F = 29.4, p<.001) while studying than males. There were no significant
differences between females and males in no-tutorial guidance group.
Conclusions and suggestions for the future research
Confirmatory factor analysis revealed the semantic meanings of items and helped to
validate factorial structure of motivational strategies in learning. Validation procedure
leaded to the conclusion that the division of a latter part of MSLIG was necessary.
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The part B (cognitive and metacognitive strategies in learning) was divided into two
different parts; resource management strategies measurement and cognitive strategies
measurement. The new tool for MSLIQ consisted of three different parts: A)
Motivational Strategies scale (MSS), B) Resource Management Strategies scale
(RMS), and C) Cognitive Learning Strategies scale (CSL). Some of the items were
reformulated and thus there is need for a new survey to test the MSLIQ. The new
model of measurement is presented in the table 1.
The tutorial guidance seemed to have positive effects on students’ motivational
strategies in learning and helping them to develop more effective cognitive learning
strategies. There where found significant differences between females and males, and
it seems to be possible to suppose that especially women seem to be able to profit
tutorial help they receive during their studies. The quality of tutorial guidance may
cause the difference between sexes and further research is needed to explore the
nature and quality of tutorial in universities.
There is still a need to develop the measurement for motivational strategies in
learning. The next step in research project is to test validated MSLIQ using it in real
virtual university courses to find out the benefits and limitations of the tool. The
research will thus continue in testing the created MSLIQ-tool in the virtual learning
environment of Finnish Virtual University. Also the measurement tool needs to be
tested again with a new survey research.
References
Gardner, H. (1993). Multiple intelligences. The theory in Practice. New York: Basic Books.
Gardner, H. and Hatch, T. 1989. Multiple intelligences go to school. Educational
Researcher, 18 (8).
IQ-Research group (2001a). A test based on Gardners’ multiple intelligence theory called: "What kind
of person am I? Department of education, University of Helsinki
IQ-Research group (2001b). Three test sets adapted from motivation tests developed by P. Pintrich and
P. Ruohotie, A test about the students view on herself as a student, a test of learning
strategies and a test about cognitive functions. Department of Education, University of
Helsinki
21
Niemi, H. (2000a, September 23). Identification of learning profiles and motivation strategies in virtual
university learning spaces. Paper presented at the European Conference on Educational
Research, 20-23 September 2000, University of Edinburgh, Scotland, U.K. Session under
the title Issues in Open and Virtual Learning, September 23rd.
Niemi, H. (in preparation). Empowering learners in Virtual University through identification of
learning profiles and motivation strategies. A paper for a book on e-Learning (in
preparation, edited by Ruohotie & Beaistro), May 2001
Pintrich, P. & Ruohotie, P. (eds.) (2000). Conative constructs and self-regulated learning. RCVE:
Hämeenlinna, Finland.
Pintrich, P. R. & McKeachie, W. J. 2000. A framework for conceptualizing student motivation and
self-regulated learning in the college classroom. In Paul Pintrich & Pekka Ruohotie (Ed.),
Conative constructs and self-regulated learning. Hämeenlinna: Research Centre for Vocational
Education. 31-50.
Pintrich, P. R. & Ruohotie, P. (ed.) 2000. Conative constructs and self-regulated learning.
Hämeenlinna: Research Centre for Vocational Education.
Pintrich, P. R., Smith, D., Garcia, T. & McKeachie, W. J. (1993) Reliability and Predictive Validity of
The Motivated Strategies for Learning Questionnaire (MSLQ). Educational and
Psychological Measurement, 53, 801-813.
Ruohotie, P. 1999. Growth prerequisties in organizations. In P. Ruohotie, H. Tirri, P. Nokelainen & T.
Silander (Eds.) Modern modelling of professional growth. Research Centre for Vocational
Education. University of Tampere.
Ruohotie, P. & Nokelainen, P. 2000. Modern modelling of student motivation and self-regulated
learning. In Paul Pintrich & Pekka Ruohotie (Ed.), Conative constructs and self-regulated learning.
Hämeenlinna: Research Centre for Vocational Education. 141-193.
Winne, P. H. & Perry, N. E. Measuring self-regulated learning. In Monique Boekaerts, Paul P. Pintrich and Moshe Zeidner (ed.) Handbook of self-regulation. San Diego: Academic Press. Contact: Senior Researcher Anne Nevgi Department of Education, University of Helsinki 00014 University of Helsinki Tel. 358 9 191 28008 Fax: 358 9 191 28073 e-mail: [email protected] IQ-FORM-research project: http://www.edu.helsinki.fi/iqform/
22
Appendix 1 The demographic background of students’ is presented in the following table 1. Table 1. Students’ demographic background, academic Success, study motivation, and received tutorial help. Frequencies (%). Variable Scale Value
Sex
Males Females Missing
127 (50,2) 126 (49,8)
3 (1,2) Age
Minimum Maximum Mean SD Missing
19 50
23.1 4.6
6 University
Helsinki Joensuu Tampere Oulu Technical University Missing
41 (16.1) 64 (25.2) 46 (18.1) 78 (30.7) 25 (9.8)
2 (0.8)
Demographic background
Major Humanities and Art Social and Behavioral Sciences Teacher Education Technology and Science Agriculture and Forest Some other Missing
6 (2.4) 39 (15.3) 46 (18.0)
117 (45.9) 35 (13.7) 12 (4.7)
1 (0.4) Academic Success
Year of Matriculation
1969-1995 1996-1998 1999-2000 Missing
62 (24.9) 134 (53.8) 53 (21.3)
7 (2.7) Matriculation
Grades Native Language (N) - mean - SD Mathematics (N) - mean - SD Humanities and Science (N) - mean - SD Second National Language (N) - mean - SD First Foreign Language (N) - mean - SD
(249) 3.98 1.17
(225) 3.67 1.33
(246) 4.07 1.26
(249) 3.72 1.41
(248) 3.31 1.34
Table continues on the next page.
23
Table continues.
Entrance Year into University
1977-1995 1996-1998 1999-2000 Missing
18 (7.2) 119 (47.4) 114 (45.4)
5 (2.0) Study credits Less than 20 credits
21 to 60 credits 61 to 100 credits More than 100 credits Missing
51 (20.0) 106 (41.6) 52 (20.4) 46 (18.0)
1 (0.4)
Study Success Very well Quite well Medium Quite poor Very poor
48 (18.8) 114 (44.5) 72 (28.1) 21 (8.2)
1 (0.4) Satisfaction with the Choice of Major
Very sure Quite sure Medium Quite unsure Very unsure
90 (35.2) 114 (44.5) 33 (12.9) 14 (5.5)
5 (2.0) Satisfaction with the present Major
Very satisfied Unsure I would like to change if possible
158 (62.2) 85 (33.5) 11 (4.3)
Study Motivation
Present Study Motivation
Very good Quite good Medium Quite poor Very poor
34 (13.3) 126 (49.2) 71 (27.7) 23 (9.0)
2 (0.8) Tutorial help during the studies
Tutorial help from teachers, tutors, or other study counselors
Very much Much Medium Quite few Not at all Missing
0 12 (4.7)
69 (27.1) 101 (39.6) 73 (28.6)
1 (0.4)