Assessing Academic Self-Regulated Learning among Filipino College Students: The Factor Structure and...

61 The International Journal of Educational and Psychological Assessment August 2010, Vol. 5

© 2010 Time Taylor Academic Journals ISSN 2094-0734

Assessing Academic Self-Regulated Learning among Filipino College

Students: The Factor Structure and Item Fit

Carlo Magno

De La Salle University, Manila

Abstract The present study constructed a self-report scale that measures academic self-regulated

learning. The Academic Self-Regulation Scale (A-SRL) was anchored on the framework of

self-regulated learning by Zimmerman and Martinez-Pons (1986; 1988). The present study

uncovered the factor structure of the A-SRL-S items and the factor structure was further

tested using Confirmatory Factor Analysis (CFA). Further psychometric evidence was

established for the scale using a Polychotomous Rasch Model (Partial Credit Model) which

determined appropriateness of the scale categories and item fit. An initial 111 items were

administered to 222 college students in the National Capital Region in the Philippines.

Principal components analysis was conducted and extracted seven factors of the A-SRL-S

which explains 42.54% of the total variance (55 items had high factor loadings). The six

factors were consistent with the original framework and a new factor called learning

responsibility emerged. This seven-factor structure was confirmed using a CFA using a

sample of 309 college students. Adequate fit of the model was attained (χ2

=332.07,

df=1409, RMS=.07, RMSEA=.06, GFI=.91, NFI=.89). The seven factors attained

convergent validity as shown by significant intercorrelations of the factor scores. The step

functions are increasing monotonically for the scale where there is a high probability of

observance of the scale categories. Only 4 out of the 55 items of the A-SRL-S lacked

homogeneity with other items. Implications of the seven factors and IRT fit of the items on

self-regulation theory was further discussed.

Keywords: Academic Self-Regulated Learning, Self-Regulation, Learning

Responsibility

Effective assessment procedure needs to take place to identify if learners

can demonstrate a repertoire of thinking strategies in order to achieve complex

learning goals. The application of teaching approaches that will facilitate learners to

become self-regulated is emphasized in the classroom setting (Magno, 2009;

Magno, 2010). Coinciding with the teaching approach is to determine how well

students have developed their self-regulated skills. Determining level of self-

regulation involves the process of assessing how well students have developed the

inclusive array of skills. Assessment provides the teacher and the learner important

information at all stages of the learning process. Self-regulation is defined by

Zimmerman (2002) as self-generated thoughts, feeling, and actions that are oriented

to attaining goals. Zimmerman (2000) further explains that self-regulated learners

are characterized to be “proactive in their efforts to learn because they are aware of

their strengths and limitations and because they are guided by personally set goals

and task-related strategies” (p. 66). In order to assess self-regulation, it must be

deconstructed further to determine its underlying skills. Zimmerman (1986)

identified several subprocesss of self-regulation to achieve ones’ personal goals. The

early conception of self-regulation is deconstructed into metacognition (planning,

organizing, self-instructing, monitoring, self-evaluating), motivation (competence,



self-efficacy, autonomy), and behavioral (select, structure, and optimize learning

environments) aspects of learning (Zimmerman, 1986). This triad was

conceptualized in line with the social cognitive theory that was used by Zimmerman

in identifying specific domains of self-regulation. This early aspects of self-

regulation was based on studies about the role of self-regulated learning on various

tasks. Recent conceptualizations of metacognition derived its subcomponents using

different clustering procedures (ex. Zimmerman & Kitsantas, 2007). The

development of a measure of self-regulation was started by Zimmerman and

Martinez-Pons (1986; 1988) using a structured interview procedure and recently

evolved into an on-line measure of self-regulatory processes (Zimmerman &

Kitsantas, 2007). To continue the development in the process of arriving at good

measures of self-regulation, an instrument was constructed in the present study that

further studied items of self-regulation using more rigorous psychometric analysis.

A Polychotomous Item Response Theory approach specifically the one-parameter

Rasch model was used to determine scale calibrations and fit of items. This analysis

allows reduction of item variances because the influence of person ability is

controlled by having a separate calibration (Wright & Masters, 1982; Wright &

Stone, 1979).

The components of self-regulation were first identified by collecting early

studies that used this construct (Zimmerman, 1986). The conceptualizations of self-

regulation later on lead to the construction of an instrument to assess self-regulation

with a multidimensional construct (Zimmerman & Martinez-Pons, 1986). The first

instrument is a structured interview of self-regulation emphasizing learning strategies

used by high school students. The interview is composed of 14 questions pertaining

to specific self-regulation skills and one additional question to determine other

strategies. Self-regulation responses were drawn in the context of classroom, home,

completing writing assignments outside of class, completing mathematic assignment

outside of class, preparing for and taking tests, and when poorly motivated. There

were 14 categories of self-regulated learning strategies identified that correspond to

each question. Reliability of the 14 categories was determined by the percentage of

agreement between two coders. Three measures were obtained for the self-

regulation strategies: Strategy use (having occurred or not), strategy frequency

(number of times a strategy was mentioned), and strategy consistency (weighted

based on the rating: seldom, occasionally, frequently, and most of the time). Some

form of discriminant validity was established in this measure where high and low

achievers were compared across the 14 categories. A discriminant function analysis

was performed and showed that students were correctly classified into high and low

achievement groups using the self-regulated learning measure. The high and low

achievement groups were significantly differentiated in most of the 14 categories.

Moreover, the high achievement group had significantly greater use of the self-

regulated learning strategies. A form of construct validity was also conducted where

the self-regulated learning scores were used to predict scores of the students in the

Metropolitan Achievement Tests (MAT) together with gender and socio-economic

status of parents. The results showed that the self-regulated learning scores

contributed largely to MAT and large variances were accounted for in the

regression.



The model arrived by Zimmerman and Martinez-Pons (1986) was further

validated in a later study using teachers’ observations of self-regulated learning

performance. Another version of the Self-regulated Learning Interview Schedule

(SRLIS) with 12 items was constructed to assess students’ self-regulated learning as

observed by their teachers (Zimmerman & Martinez-Pons, 1988). The 12 items

were drawn from 6 self-regulated learning contexts. This instrument was called the

Rating Student Self-regulated Learning Outcomes: A Teacher Scale (RSSRL). In

this measure, students were rated using a 5-point scale (never=1 to always=5). A

principal components analysis with oblique and orthogonal rotation was conducted

with the 12 items together with the two subtests of the MAT (Mathematics and

English). A three factor structure was uncovered where most of the RSSRL items

loaded in the first factor (student self-regulated learning), organizing and

transforming strategies (novel comments from students) loaded in the second

factors (student verbal expressiveness), and the Mathematics and English subtests of

the MAT loaded on the third factor (student achievement). The three factors were

significantly correlated indicting their convergence. The SRLIS scores were

correlated with the three factors of the RSSRL using a multivariate canonical

correlation to determine the validity of the scales. The SRLIS correlated with factor

1, while the SRLIS negatively correlated with factors 2 and 3 of the RSSRL.

Zimmerman and Martine-Pons (1988) explained that “the canonical correlation

between SRLIS and factor 1 was enhanced by the elimination of the variance

attributable to factors 2 and 3” (p. 288). They further explained that the negative

canonical correlations are indicative of discriminant validity where self-regulated

learning is differentiated with verbal expressiveness and achievement.

The factors of SRLIS were further developed by Zimmerman and

Martinez-Pons (1990) where they found differences across gender and giftedness.

Females significantly had higher goal-setting and planning, and keeping records and

monitoring as compared to males. Gifted students significantly had higher

organization and planning, keeping records and monitoring, seeking teacher

assistance, and reviewing notes. A developmental pattern also occurred where 8th

graders surpassed the 5th graders on almost all strategy use. A regression was

conducted and it was found that the factors of SRLIS were significantly correlated

with mathematics efficacy and verbal efficacy. These results indicate that the factors

of the SRLIS can be discriminated by gender, giftedness, and age groups. The same

pattern was observed by Magno and Lajom (2008) where the relationship between

goal-setting and planning with performance approach is stronger for college

students than high school students.

In other studies of Zimmerman with colleagues, some factors of self-

regulation was assessed with different protocols such as the attribution scale (Cleary,

Zimmerman, & Keating, 2006; Kitsantas, Zimmerman, & Cleary, 2000;

Zimmerman & Kitsantas, 1997; 1999), intrinsic interest (Kitsantas, Zimmerman, &

Cleary, 2000; Zimmerman & Kitsantas, 1999; 2002), self-evaluation (Cleary,

Zimmerman, & Keating, 2006), adaptive inferences scale (Cleary & Zimmerman,

2001; Cleary, Zimmerman, & Keating, 2006), and perceived responsibility for

learning (Zimmerman & Kitsantas, 2005). These measures usually ask respondents



about their feelings and beliefs about changes in their self-regulated learning which

was called microanalytic measures (see Zimmerman, 2008).

Zimmerman (2008) also reported several emergent assessment protocols of

self-regulated learning (SRL). These recorded assessment procedures are trace logs

of SRL processes in computer-assisted environments (Winne et al., 2006), think-

aloud protocols in hypermedia environments (Greene & Azevedo, 2007),

structured diary (Schmitz & Weise, 2006), observations and qualitative measures

(Perry, Vandekamp, Mercer, & Nordby, 2002), and microanalytic measures.

Winne and Perry (2000) reported seven different protocols of assessing self-

regulation. These assessment techniques are: Questionnaires, structured interview,

teacher judgments, think aloud techniques, error detection tasks, trace

methodologies, and observation of performance. These classifications are based on

Winne and Perry’s (2005) assessment of self-regulation as aptitude and event.

Magno (2009) explained that before using any of the protocols in assessing self-

regulation, users must be critical of the methods and rigors on how the tools were

established which concerns their validity and reliability. The process of establishing

the scales first involve the construction and selection of items based on a

framework, an empirical model, or grounded on some empirical data. The

underlying factors of the items are then explored using Exploratory Factor Analysis

(EFA) techniques. The underlying factors are further

tested by using a more rigorous method like Confirmatory Factor Analysis (CFA).

On some instances, the test developer may opt to use a different approach such as

the Item Response Theory (IRT). In this approach, items are good if they have

acceptable item characteristic curves based on the logit measures. In such cases,

items with good fit (Mean Square within 0.8 to 1.2, z standard score of below 2.00),

high point biserial correlations (indicative of item discrimination for a one-

parameter Rasch model), adequate item information functions, and devoid of item

differential functioning (free of bias) are considered as acceptable items. On the

second criteria, responses to items should indicate an acceptable reliability or

consistency. Most commonly, internal consistencies of test are established using

Cronbach’s alpha, split-half, or interitem correlation. Tests and scales of self-

regulation evidenced to have acceptable validity and reliability are safe to use.

The most common scale used in some studies that measure self-regulation

is the Learning and Study Strategies Inventory (LASSI, Weinstein, Shulte, &

Palmer, 1987) and the Motivated Strategies for Learning Questionnaire (MLSQ,

Pintrich, Smith, Garcia, & McKeachie, 1993). These measures reflect the learning

strategies, study practices, and metacognition that are subsumed in self-regulated

learning. However, these scales are generally measuring specific learning constructs

where LASSI is used for assessing learning strategies and the MLSQ generally

measures motivation and learning strategies as well.

There is a need to come up with a specific scale to measure self-regulation

as conceptualized by Zimmerman and Martinez-Pons (1986; 1988). Having a

specific scale for self-regulated learning addresses the gap posed by confounded

traits measured by the LASSI and MLSQ. Magno (2009) point the advantage of

using a scale as an economical way of administering, scoring, and interpreting



information. Scales can be administered to numerous students at a single time. This

ensures consistency in the instructions given for respondents and

further control the testing condition. Scores can be obtained by computing for

means on certain factors. The numerical scores are easily interpreted by

constructing norms for groups or setting standards for interpreting scores.

Generally, high scores indicate the optimum presence of self-regulation

characteristics measured and low scores indicate less of the characteristic.

The present study constructed a scale anchored on the conceptualization

and factors of self-regulated learning by Zimmerman and Martinez-Pons (1986;

1988). Aside from confirming the factor structure of the scale, item functioning is

analyzed using a polychotomous item response theory which provides a more

rigorous psychometric analysis.

Method

Participants

The first set of participant in the study is composed of 222 college students

from different universities in the National Capital Region in the Philippines (100

males and 122 females). The average age of the participants is 17.8. All participants

came from a private university. All participants reported ability to read, write, and

comprehend texts written in the English language.

Another set of sample was used to confirm the factors derived in the

previous analysis. The second set of participants was composed of 309 college

students having the same characteristics of the initial sample (151 females and 158

males).

Instrument

Items were generated that measure academic self-regulated learning based

on the responses of 1454 college students on eight protocols (see Magno, 2010).

These eight protocols were based on the six self-regulated learning contexts

developed by Zimmerman and Martinez-Pons (1988). There were several answers

that were similar and 111 items were generated based on the responses of the

students. The items were classified according to the 14 categories of the SRLIS

(self-evaluation, organizing and transforming, goal setting and planning, seeking

information, keeping records and monitoring, environmental structuring, self-

consequences, rehearsing and memorizing, seeking peer assistance, seeking teacher

assistance, seeking adult assistance, reviewing tests, reviewing notes, and reviewing

texts). The items were reviewed by two educational psychologists doing research on

self-regulation. The items were reviewed whether they are within the scope of the

definition of the factors of self-regulation. The items were revised based on the

feedback provided in the review. Each item is answered using a four-point scale

(strongly agree=4, agree=3, disagree=2, strongly disagree=1). The items were further

reduced based on an initial principal components analysis. The factors extracted

were confirmed in another sample. Finally the items were calibrated based on the

Polychotomous Rasch Model (Partial Credit Model).



Procedure

There was an initial sample of 1454 college students who were interviewed

using the revised SRLIS with eight protocols (see Magno, 2010). Students were

further probed if they are unable to provide a response indicative of self-regulation

strategy. During the interview students were asked to contextualize their answer in

the school-related activities. This is done because the instrument was limited to

assess self-regulated learning in the academic setting (or school setting or school-

related thoughts). Three educational psychology students were trained to cluster the

responses from the revised SRLIS to the 14 categories of self-regulated learning.

The specific responses were first independently clustered by each trained student.

The students were guided by the indicators provided by Zimmerman and Martinez-

Pons (1986) for the 14 categories. The percentage of consistent clusters was

obtained. There was an agreement of 92% for the responses clustered. The items

were written based on the clusters formed. The items were initially administered to

222 college students and principal components analysis was conducted to uncover

the factor structure of academic self-regulated learning for Filipino college students.

The extracted factor structure was later confirmed (using Confirmatory Factor

Analysis) to another sample composed of 309 students.

In administering the constructed scale, students were reminded to answer as

honestly as possible and not to take too much time in answering some items. The

students were reminded to answer as honestly as possible and make sure to

complete all items. Students were debriefed about the purpose of the study before

the scale was administered.

Item analysis was conducted for each factor by the estimation of Rasch

item and person fit scores. The Rasch model ensures that each factor is

unidimensional and do not contain sources of variations. The software

WINSTEPS was used for the Rasch model item analysis. The analysis determined

(a) if the difficulty levels of the items reflect the full range of respondents' trait

levels, and (b) how well the 4-point scale captures the distinctions between each

category of agreement. This software package begins with provisional central

estimates of item difficulty and person ability parameters, compares expected

responses based on these estimates to the data, constructs new parameter estimates

using maximum likelihood estimation, and then reiterates the analysis until the

change between successive iterations is small enough to satisfy a preselected

criterion value (Linacre, 2006). Although, the estimates are called difficulty which

refers to correct responses (such as ability measures), the Rasch model is applicable

for non-cognitive measures where difficulty would refer to extreme low scores in a

measure.

Results

A principal components analysis was conducted among the 111 items of the

Academic Self-Regulation Scale (A-SRL-S). An examination of the scree plot

showed that seven factors can be produced. The seven factors extracted accounts

for 42.54% of the total variance. The remaining factors extracted were not



considered because the same total variances were produced and were also low. The

varimax rotation method was used because it accounted for larger factor loadings

under each of the four factors extracted. The items with factor loadings below .40

were removed and 55 items were retained. The 55 items were classified under each

of the new factor solution: Memory strategy (14 items), goal-setting (5 items), self-

evaluation (12 items), seeking assistance (8 items), environmental structuring (5

items), learning responsibility (5 items), and organizing (6 items). The six extracted

factors were in place with the categories of the original SRLIS but a new factor

emerged which was labeled as learning responsibility (e. g., I accomplish the task as

soon as the teacher gives it, I am concerned by the deadlines set by my teacher, I

finish my homework first before doing other things).

The seven factors of the A-SRL-S were confirmed in another sample

(N=309) with similar characteristics as to the first sample. A seven-factor model was

tested where all 55 items were used as indicators. The seven latent variables were

intercorrelated in the measurement model to provide evidence of their

convergence. The results of the CFA showed that all seven latent factors were

significantly correlated (p<.001) and all items that belong to each of their factor all

had significant estimates (p<.001). The model also attained adequate fit as shown by

the chi-square (χ2

=332.07, df=1409), Root Means Square Standardized Residual

(RMS=.07), Root Mean Square Error Approximation (RMSEA=.06), GFI=.91, and

Bentler-Bonett Normed Fit Index (NFI=.89).

Table 1

Descriptive Statistics of the Factors of A-SRL-S

N M CI -95%

CI

95% SD SE

Cronbach's

alpha

Person

Reliability

Item

Reliability

Memory Strategy 309 2.53 2.48 2.59 0.50 0.03 0.82 .80 .99

Goal Setting 309 2.74 2.65 2.83 0.81 0.05 0.87 .76 .89

Self-evaluation 309 2.84 2.78 2.90 0.50 0.03 0.83 .81 .98

Seeking Assistance 309 3.12 3.07 3.18 0.49 0.03 0.74 .66 .98

Environmental

Structuring 309 2.82 2.75 2.90 0.68 0.04 0.73 .65 .97

Learning

Responsibility 309 2.96 2.89 3.02 0.59 0.03 0.75 .67 .97

Organizing 309 3.26 3.19 3.32 0.57 0.03 0.78 .61 .83

Note. The instrument used a four-point scale (strongly agree=4, agree=3,

disagree=2, strongly disagree=1)

Each item was answered using a four-point scale and high mean scores were

obtained for seeking assistance and organizing as compared to the other A-SRL-S

factors. Minimal variances were obtained among the distribution of scores for each

factor as indicated by the low standard deviations and standard errors. Very high

internal consistency was also obtained for all factors. Person and item reliability was

obtained separately in the Rasch analysis. Very high consistencies were also

obtained for both person and item responses.



Table 2

Intercorrelations of the Factors of the A-SRL-S

1 2 3 4 5 6 7

(1) Memory Strategy ---

(2) Goal Setting .46** ---

(3) Self-evaluation .55** .32** ---

(4) Seeking Assistance .39** .27** .49** ---

(5) Environmental Structuring .26** .25** .35** .31** ---

(6) Learning Responsibility .42** .28** .48** .44** .41** ---

(7) Organizing .41** .42** .35** .41** .38** .51** ---

**p<.01

When the seven factors of the A-SRL-S were intercorrelated, all

correlations were significant, p<.01. The moderate correlation coefficients (.25-.55)

indicate that the factors are not multicollinear to each other. The positive direction

indicates the convergence of the seven A-SRL-S factors. The significant

intercorrelations were consistent with the correlations found in the CFA.

To investigate the functioning of the items in the A-SRL-S, the one-

parameter Rasch model was used. The scale categories (4-point scale) were first

analyzed in the process to determine the threshold. Higher scale categories must

reflect higher measures and low values for lower scales, thereby producing a

monotonic increase in threshold values.

The average step calibrations for memory strategy are, -1.57, .25, 1.71, and

3.41, for goal setting, -3.19, -.92, 1.37, and 3.61, for self-evaluation, -2.71, -.59, 1.25,

and 3.15, for seeking assistance, -2.70, -1.06, .41, and 2.30, for environmental

structuring, 2.32, -.42, 1.40, and 3.47, for responsibility, -3.43, -1.20, .98, and 3.98,

and for organizing, -2.88, -.95, .79, and 2.76. All average step functions are

increasing monotonically indicating that a 4-point scale for each item attained “scale

ordering” where there is a high probability of observance of certain scale categories.

To determine if the items under each factor has a unidimensional structure,

item fit mean square (MNSQ) was computed. MNSQ INFIT values within 1.2 and

less than 0.8 are acceptable. High values of item MNSQ indicate a “lack of

construct homogeneity” with other items in a scale, whereas low values indicate

“redundancy” with other items (Linacre, 2006). Four Rasch analyses were

conducted separately for each factor. Most of the items fitted the Rasch model.

Very few items turned out to have a bad fit (see Appendices A-G). For memory

strategy, all items had a good fit except for one item which is almost marginal with

infit MNSQ of 1.2 (I use note cards to write information I need to remember). All

other items for goal setting, self-evaluation, seeking assistance, and learning

responsibility fitted the Rasch Model. For environmental structuring, one item (I

can’t study nor do my homework if the room is dark) with an infit of 1.65 also lacks

construct homogeneity. For organizing, two items also lacked construct

homogeneity (I put my past notebooks, handouts, and the like in a certain

container; I study at my own pace). Items that lack construct homogeneity do not



share a similarity with the pool of items in the factor. These items can either be

removed or revised.

Discussion

The study developed an Academic Self-Regulated Learning Scale (A-SRL-

S) which is a self-report measure anchored on the conceptualization and factors of

Zimmerman and Martinez-Pons’ (1986; 1988) framework. The factor structure of

the scale was uncovered and later confirmed. The items were also calibrated using a

partial credit model (polychotomous Rasch model). An item response theory

approach was applied for the items to provide better evidence of the scales’

psychometric property. It was found that the items of the A-SRL-S loaded under

seven factors (memory strategy, goal setting, self-evaluation, seeking assistance,

environmental structuring, learning responsibility, and organizing). The six factors

were consistent with the original framework of Zimmerman and Martinez-Pons

(1986; 1988) and a new factor which is learning responsibility emerged. Good

evidence of validity and reliability was obtained for the A-SRL-S. The CFA showed

that all 55 items had significant parameter estimates for each latent factor, all the

seven factors were significantly correlated, and adequate goodness of fit of the

model was well-represented by the observations (N=309). The obtained good fit of

the model supports a seven-factor structure of the A-SRL-S. The CFA and

correlations of the seven factors of the A-SRL-S is indicative of convergent validity

of the scale. The results of the IRT using a partial credit model showed that there

was a valid observance of the 4-point scale categories. Out of the 55 items, only 4

items showed to have lack of construct homogeneity. All the rest of the items fitted

the Rasch model.

It was initially argued at the onset of the study that using a scale to measure

self-regulated learning was efficient which justifies its usefulness. Because of the

consistency and control as an advantage of administering self-report scales, the

influence of error variances were minimal. This was evident in the estimation of

confidence intervals (see Table 1), standard errors of the factor score distribution

(see Table 1), and standard errors of item calibration (see Appendices A-G).

Minimizing standard errors is one of the primary concerns in both classical test

theory and item response theory approach. The IRT that was used in the study was

more advantageous in minimizing standard errors for each item because the item

difficulties (logit measure) were calibrated separately with the person ability. This

technique renders the items of the A-SRL-S sample free and ability free. As

compared to previous techniques in establishing the reliability and validity of the

self-regulation measures, probability and reliability estimations were not devoid of

the error terms.

A new model of academic self-regulated learning composed of seven factors

was derived in the study. The composition of the factors derived in the study is

similar with the framework of Zimmerman and Martinez-Pons (1986; 1988).

However, a new factor emerged from the initial factor analysis. A factor on learning

responsibility was extracted and this is composed of items on rechecking homework

if it is done correctly, doing things as soon as the teacher gives the task, having

concern with deadlines, prioritizing schoolwork, and finishing all homework first.



The items reflect learners’ liability, accountability, and conscientiousness of the

learning task and learning experience. This emergent factor is supported in the

previous studies of Zimmerman and Kitsantas (2005; 2007). Zimmerman and

Kitsantas (2005) developed a perceived responsibility for learning scale in their

previous study and it was used in a later study with another form of a self-efficacy

measure (see Zimmeman & Kitsantas, 2007). The items in their scale identified

who is more responsible (student or teacher) on three learning context: Student

motivation, deportment, and learning process. The perceived responsibility scale

they have developed also had acceptable validity and reliability indices. It can be

argued that responsibility for learning is part of self-regulation. First, the items in the

scale of Zimmerman and Kitsantas (2005) are reflective of self-regulated learning.

In the example of items they provided, learning strategies such as taking notes in

class, interest in school, and remembering information are indicative of self-

regulation strategies. Second, perceived responsibility significantly correlated with

the RSSRL which indicates their convergence and possibly their amalgamation.

Both conceptual and psychometric evidence on the role of learning responsibility

as part of the of self-regulated learning is justified. Further studies need to be

developed to prove its conjoint relationship with the rest of the original factors.

Self-regulated learning originally is composed of 14 categories. In the

present study, seven factors of self-regulated learning were extracted. This shows

that the factors extracted in the study which as consistent in the original framework

are strong indicators of academic self-regulated learning. These factors that are

considered as strong indicators are self-evaluation, organizing, goal setting, seeking

assistance, environmental structuring, and memory strategy. The other components

that did not emerge as separate factors are seeking information, keeping records,

self-consequence, and reviewing. It was expected that the 14 or possibly more

factors will emerge considering that more contexts were asked among the students

in the initial interview. There were 8 learning contexts asked as compared to the 6

learning context used by Zimmerman and Martinez-Pons (1988). The items on the

remaining components were mixed with other factors. It can be considered that

specific skills such as seeking information, keeping records, and reviewing are all

used in other learning strategies such as organizing, setting goals, environmental

structuring, and memory strategy. These skills when joined with the seven factors

mean that they are specific skills that manifest other self-regulation factors. It can

also be noted that the components are not independent in terms of their utility for

Filipino college learners. For example, a typical college student in the Philippines

when reviewing uses memory strategies. When a learner organizes their learning

materials, they keep record for future or immediate use. The relationship among

the seven indicators of self-regulated learning with the four other factors needs

further investigation to fully explain their pattern of independence.

The seven-factor structure of A-SRL-S was confirmed having a good fit. The

factorial validity of the seven factors of self-regulation is further proven to be

adequate for the Filipino college sample. This indicates that the seven factors which

are convergent with each other are represented well by the Filipino college students

who answered the items in the scale. This also indicates the usefulness of the six

strong indictors of self-regulation with the complement of learning responsibility as



another factor. This shows that the Filipino college student (in a private university)

in learning a task makes sure that they are responsible for their learning which

coincide with their use of other learning strategies. The specific items used in the

study supports the goof fit found in the CFA. The item analysis in the Rasch model

showed that almost all items had a good fit. The learning contexts manifested by the

items to measure self-regulated learning were appropriate across the learners.

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Appendix A

Memory Strategy

Infit Outfit

Items Measure SE

MS

Q Z MSQ Z PB

1 I use note cards to write information I

need to remember. 0.96 0.08 1.20 2.64 1.22 2.63 0.43

2 I make lists of related information by

categories. -0.4 0.08 0.83 -2.33 0.83 -2.37 0.64

3 I rewrite class notes by rearranging the

information in my own words. -0.2 0.07 1.01 0.1 1.01 0.21 0.57

4 I use graphic organizers to put abstract

information into a concrete form. 0.52 0.08 0.95 -0.64 0.94 -0.75 0.58

5 I represent concepts with symbols such as

drawings so I can easily remember them. 0.09 0.07 1.01 0.22 1.02 0.32 0.56

6 I make a summary of my readings. -0.61 0.08 0.86 -1.97 0.84 -2.19 0.64

7 I make outlines as guides while I am

studying. -0.75 0.08 0.9 -1.42 0.88 -1.59 0.62

8 I summarize every topic we would have in

class. 0.01 0.08 0.84 -2.39 0.82 -2.53 0.66

9 I visualize words in my mind to recall

terms. -1.31 0.09 1.22 2.68 1.17 2.02 0.39

10 I recite the answers to questions on the

topic that I made up. -0.2 0.07 1 0.03 1 0.01 0.56

11 I record the lessons that I attend to. 1.75 0.09 1.18 1.49 1.92 3.97 0.37

12 I make sample questions from a topic and

answer them. 0.74 0.07 0.89 -1.56 0.88 -1.51 0.62

13 I recite my notes while studying for an

exam. -0.67 0.07 1.05 0.68 1.07 0.86 0.53

14 I write messages for myself to remind me

of my homework. 0.06 0.07 1.12 1.69 1.2 2.59 0.51

Note. Measure=Logistic Measure for item difficulty, MSQ=Mean Square,

PB=Point Biserial

Appendix B

Goal Setting

Infit Outfit

Items Measure SE

MS

Q Z MSQ Z PB

1 I make a detailed schedule of my daily

activities. 0.22 0.1 0.89 -1.37 0.88 -1.42 0.83

2 I make a timetable of all the activities I

have to complete. 0.29 0.1 1.12 1.48 1.11 1.28 0.79

3 I plan the things I have to do in a week. -0.19 0.1 0.99 -0.04 1 0.04 0.8

4 I use a planner to keep track of what I am

supposed to accomplish. 0.19 0.09 0.98 -0.22 0.97 -0.32 0.8

5 I keep track of everything I have to do in

a notebook or on a calendar. -0.51 0.1 0.99 -0.09 1.01 0.15 0.79


PB=Point Biserial



Appendix C

Self-Evaluation

Infit Outfit

Item Measure SE

MS

Q Z MSQ Z PB

1 If I am having a difficulty, I inquire

assistance from an expert. 0.29 0.08 1.17 2.18 1.19 2.32 0.53

2 I welcome peer evaluations for every

output. 0.45 0.08 1.18 2.35 1.26 3.1 0.52

3 I evaluate my accomplishments at the

end of each study session. 0.54 0.08 0.99 -0.04 1.02 0.24 0.6

4 I ask others how my work is before

passing it to my professors. 0.58 0.08 0.99 -0.13 0.97 -0.3 0.63

5 I take note of the improvements on

what I do. 0.67 0.08 0.93 -1 0.92 -1.07 0.65

6 I monitor my improvements in doing

certain task. 0.25 0.09 0.91 -1.21 0.9 -1.26 0.65

7 I ask feedback of my performance from

someone who is more capable. 0.25 0.08 0.87 -1.76 0.89 -1.37 0.66

8 I listen attentively to people who

comment on my work. -0.83 0.09 1 -0.01 0.98 -0.2 0.55

9 I am open to feedbacks to improve my

work. -1.11 0.1 1.01 0.1 0.95 -0.46 0.51

10 I browse through my past outputs to see

my progress. -0.16 0.09 0.92 -1 0.93 -0.9 0.62

11 I ask others what changes should be

done with my homework, papers, etc. 0.05 0.08 0.88 -1.6 0.88 -1.61 0.65

12 I am open to changes based from the

feedbacks I received. -0.99 0.1 1.11 1.37 1.18 1.94 0.47


PB=Point Biserial

Appendix D

Seeking Assistance

Infit Outfit

Item Measure SE

MS

Q Z MSQ Z PB

1 I use a variety of sources in making my

research papers. -0.28 0.09 0.98 -0.26 0.98 -0.12 0.55

2 I use library resources to find the

information that I need. -0.7 0.1 0.94 -0.7 0.9 -0.88 0.54

3 I take my own notes in class. -0.76 0.09 1.22 2.5 1.35 2.97 0.41

4 I enjoy group works because we help

one another. 0.03 0.08 0.99 -0.07 1.03 0.34 0.58

5 I call a classmate about the homework

that I missed. -0.22 0.09 0.87 -1.66 0.9 -0.94 0.59

6 I look for a friend whom I can have an

exchange of questions 0.04 0.09 0.89 -1.52 0.87 -1.63 0.65

7 I study with a partner to compare notes. 1.09 0.08 1.13 1.66 1.11 1.4 0.6

8 I explain to my peers what I have

learned. 0.8 0.08 0.98 -0.23 0.98 -0.27 0.64


PB=Point Biserial



Appendix E

Environmental Structuring

Infit Oufit

Item Measure SE

MS

Q Z MSQ Z PB

1 I avoid watching the television if I have a

pending a homework. 0.52 0.08 0.96 -0.49 0.94 -0.7 0.71

2 I isolate myself from unnecessary noisy

places. -0.32 0.09 0.78 -3.04 0.79 -2.81 0.74

3 I don’t want to hear a single sound when

I’m studying. 0.6 0.08 0.99 -0.1 0.96 -0.45 0.71

4 I can’t study nor do my homework if the

room is dark. -0.69 0.08 1.65 6.55 1.76 5.07 0.46

5 I switch off my TV for me to concentrate

on my studies. -0.11 0.08 0.71 -4.2 0.68 -4.31 0.78


PB=Point Biserial

Appendix F

Learning Responsibility

Infit Outfit

Items Measure SE

MS

Q Z MSQ Z PB

1 I recheck my homework if I have done

it correctly before passing. -0.15 0.09 0.96 -0.5 1 0.03 0.7

2 I do things as soon as the teacher gives

the task. 0.83 0.1 0.91 -1.12 0.91 -1.13 0.76

3 I am concerned with the deadlines set

by the teachers. -0.85 0.1 0.99 -0.09 0.9 -0.76 0.64

4 I prioritize my schoolwork over other

activities. -0.34 0.1 1.19 2.12 1.21 2.23 0.63

5 I finish all my homework first before

doing unnecessary things. 0.5 0.1 0.94 -0.68 0.94 -0.79 0.75


PB=Point Biserial

Appendix G

Organizing

Infit Outfit

Item Measure SE

MS

Q Z MSQ Z PB

1 I highlight important concepts and

information I find in my readings. -0.07 0.1 0.83 -2.01 0.84 -1.76 0.7

2 I picture in my mind how the test will look

like based on previous tests. 0.08 0.09 0.91 -1.06 0.85 -1.57 0.68

3 I put my past notebooks, handouts, and

the like in a certain container. 0.26 0.09 1.22 2.46 1.28 2.96 0.61

4 I study at my own pace. -0.46 0.1 1.24 2.54 1.25 2.51 0.55

5 I fix my things first before I start studying. -0.02 0.1 0.8 -2.59 0.77 -2.87 0.74

6 I make sure my study area is clean before

studying. 0.22 0.09 1 -0.01 0.98 -0.22 0.69


PB=Point Biserial



Author Note

I would like to acknowledge my two self-regulated practicum students Ms. Sheena

Morales and Ms. MR Aplaon for their assistance and effort in all aspects in the

completion of this study.

I also like to acknowledge the University Research and Coordination Office

(URCO) of De La Salle University, Manila for providing the grants for this study.

About the Author

Dr. Carlo Magno is presently a faculty of the Counseling and Educational

Psychology Department at De La Salle University, Manila. He teaches courses in

quantitative research, statistics, psychometric theory, and assessment of learning.

His research interest includes self-regulation, metacognition, language learning,

parenting, scientific thinking, epistemological beliefs, and parenting all using

quantitative analysis.

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