Creative Education
2012. Vol.3, No.1, 41-44
Published Online February 2012 in SciRes (
Copyright © 2012 SciRes. 41
Relevance of Mastery Learning (ML) in Teaching of English
(Case Study of the University of Guilan, Iran)
Abbas Sadeghi1, Atefeh Sadeghi2
1Department of Educational Sciences, The University of Guilan, Rasht, Iran
2The University of Guilan, Rasht, Iran
Received December 20th, 2011; revised January 18th, 2012; accepted January 30th, 2012
The main purpose of the study was to gather, analyze and interpret the perceptions of the students about
mastery learning (ML) held by 240 students randomly selected from each of the populations of different
faculties in Guilan University. Guilan University was chosen because the researchers have some valuable
experiences about English learning and are familiar with students’ weakness in English learning. The stu-
dents of high ability were allocated to “A” and “B” classes, average to “C” and “D” classes and low to
“E” and “F” classes respectively. Two Academic Staff Members were assigned to teach the six classes of
English. Students could take 3 classes with each academic. Results showed that based on research results
on deep and surface, biased learners increasingly which became surface learners did worse compare with
deep learners. On the other hand, surface students of low ability seem to be motivated to study as they are
given more chances to secure a pass. Thus, although the findings of this paper indicate that mastery
learning promotes better quantitative results in English for surface learners, there are dangers. One of the
main aims of learning to increase higher level cognitive processes seems actually to be discouraged in this
Keywords: Learning; Mastery Learning; Surface Learning; Deep Learning
Iranian universities are spending much time and effort to pro-
vide their students with the best learning experience possible.
This effort becomes particularly important when framed within
the idea that students usually have more than one choice for
their class career, leading to competition between universities.
In order to make their programs more attractive to current stu-
dents, the University of Guilan have begun a fundamental shift
in how their classes are c onducted by implementing internal and
ext ernal assessments. Often, these universities have moved away
from the traditional teacher-based instruction in favor of more
ac tive, learner-centered activitie s. It is believed that more lea rner-
centered and collaborative activities will enhance a ML experi-
ence. Though, a positive learning experience could be defined
by a number of factors the use of deep learning strategies are
believed to be i ntegral to a ML experie nce. Corno and Mandinach
(1983) were the first researchers to define and examine student
participation. They proposed that student participation was evi-
dent when students demonstrated prolonged attention to a men-
ta l ly challenging task, resulting in authentic learning and increased
levels of higher order thinking. Indeed, Conrad and Donaldson
(2004) stated that critical thinking is a result of high levels of
participation as a signal of ML.
Deep learners can transfer the learned concepts to a variety
of situations thereby creating a denser matrix of connections
within their understanding. Therefore, the students’ motives are
integral to whether they engage in deep or surface learning stra-
tegies. Though, there are a number of factors related to ML and
perception of learning strategies are among the most important.
While cognitive engagement and perception of course value sug-
gest motives for learning, learning strategies are what the stu-
dents do relative to those motives (Biggs, 1987). Deep and sur-
face learning strategies are motivated by different factors and
would be expected to move in a coherent pattern in relationship
to each other: Students who use deep learning strategies would
tend not to use surface strategies, and students who use surface
learning strategies would tend not to use deep strategies (Cano,
20 07). T hus, learning strategie s are affected by learning approach.
Research has shown that shifting from traditional teacher cen-
tered to a more learner-centered approach leads to deeper levels
of learning (Tagg, 2003).
In their study of undergraduate students, Robinson and Hull-
inger (2008) found that successful students, defined as those who
averaged an A grade, and students who were satisfied with their
university experience reported higher levels of participation. Re-
searchers have often paired the factors of course value as a sym-
bol of ML and learning in their study of student evaluations of
teaching (Marsh & Roche, 2000).
According to “Davis & Sorrel” (1995) the ML concept have
increased in American schools in 1920’s with the work of Wa-
shburn and others in the format of Winnetka plan. ML is based
on the assumption that learning is a function of time, the learn-
ing history of a student and the quality of instruction (Bloom,
1976) and also, is anchored in the work of Bloom (1981) often
associated with the emphasis on standards-based curriculum. It
was develope d as a way for teachers to provide more appropri-
ate instructional strategies for their students. Guskey (1985, 2007)
be lieved under these more favorable learning conditions; the the-
ory was that nearly all students would be able to teach a subject
to the point of “mastery” and combine teacher expertise and re-
sources to enhance the classroom environment and collaboration.
The term “ML” refers to a divers category of instructional
methods but the principal defining characteristics are: the es-
ta blishment of a criterion level of performance to represent “mas-
tery” of a given skill or concept frequent assessment of student
progress and provision of corrective instruction. In order to en-
sure that most students are able to master instructional objecti-
ves time and resources are reorganized; those failing to reach
the objectives initially are given more time in which to do so in
subsequent attempts. Bloom (1976) also includes an emphasis
on appropriate use of such instructional variables as cues par-
ticipation feedback and reinforcement as elements of ML.
There are three primary forms of ML. The Personalized Sys-
tem of Instruction (PSI) or the Keller Plan and Continuous Pro-
gress (Cohen, 1977) where students work on individualized units
en tirely at their own speed. The third form of ML is called group
based ML or Learning For Mastery (Block & Anderson, 1975),
commonly used in elementary and secondary schools and it is
adapted for the present study.
Deep learning can be reached when attention and motivation
are present. The process of participation is as an important a fac-
tor in informing our students as the quality and usefulness of
the task at hand. Initial interest in learning can be triggered by
personal relevance (Hidi & Renninger, 2006). However indi-
vidual interest may diminish if not supported and true participa-
tion may not result. Well-developed individual interest tends to
be psychologically based and affective but is still facilitated by
instructional conditions, such as opportunities for interaction
and ML Hulleman (2007) found that a relevance intervention,
where students were encouraged to apply the course material to
their own lives, increased perception of value, leading to increased
interest and classroom performance, particularly among students
with lower levels of belief in their abilities.
The academic staffs instruct the entire class at one pace. At
the end of each unit of instruction a “formative” test is given
with a mastery criterion usually in the range of 80% - 90% cor-
rect. Any students who do not achieve the mastery criterion re-
ceive corrective instruction which may take the form of tutoring
by the teacher or by students who did achieve the criterion level.
Corrective activities are different from the kinds of actives used
in the initial instruction as suggested by Block and Anderson.
Following the corrective instruction students take a parallel test.
The class, then moves on even if several students still have not
got a passing score. All students who achieve the mastery crite-
rion at any point are generally given an “a” on the unit regard-
less of how many attempts it took for them to reach the crite-
rion score.
The Importance of Mastery Learning
According to “Zimmerman & Dibendetto” (2008) ML uses
differentiated and individualized instruction, progress monitor-
ing formative assessment, feedback, corrective procedures and
instructional alignment to memorize achievement gaps.
There have been many studies of the effectiveness of ML and
teaching strategy recently reviewed and evaluated in a Meta
analysis by Kulik, Kulik and Bangert-Drowns (1990). With re-
gard to final examination or test performance it was found in 67
out of 96 studies that the performance of students in mastery
programs was significantly higher than in control classes the
re maining differences being no significant. In no case were mas-
tery groups significantly worse off than controls. Gains in mas-
tery groups were greatest for low ability students. Best results
were found when using locally designed tests rather than stan-
dar dized test. Less research seems to have been c onducted relat-
ing students approaches to learning or even the cognitive level
of learning outcomes to ML programs. Given the apparent suc-
cess of ML this is a serious gap as it could be that success is
bought at the price of leaning quality.
This possibility is raised because the design of ML programs
would seem to encourage surface learning as success is defined
in terms of passing test items usually quite specific to the con-
tent taught. Although each test attempt is contingent on success
in a previous test students are not encouraged to integrate mate-
rial or even to remember material previously tested but not in
the upcoming unit. Further test items tend to be of a low cogni-
tive level because of the requirements of precise and frequent
testing (Cole 1990).
Main Objectives
This study focuses on the teaching of English for students in
Guilan University, using ML. The objectives of the study are:
1) To look at the effects of ML on the learning outcomes in
students with different learning approaches in learning English
2) To look at the effects of ML on the cognitive level of the
outcomes elicited.
The participants of this study consisted of 240 students in
different faculties in Guilan University. The students of high
ability were allocated to “A” and “B” classes, average to “C”
and “D” classes and low to “E” and “F” classes respectively.
Two academic staff members were assigned to teach the six
classes of English students, with each academic taking 3 classes.
The assignment of teaching duties to the various classes is stated
in Table 1 with students’ respective mean English scores in
Humanities and English attainment test scores from the previ-
ous year.
Data Collection
Learning Process Questionnaire (LPQ)
At the beginning of the term, the LPQ was given to all stu-
dents. The raw scores were then coded as deciles scale scores.
Afterwards, the students were classified into surface learners
and deep learners’ categories accordingly. The basis of classify-
cation was as follows:
1) Surface learners. Surface deciles scale score is greater than
deep deciles Scale score by two.
2) Deep learners. Deep deciles scale score is greater than
surface deciles scale score by two.
Table 1.
Mean scores of English scores in Humanities (HU) and English scores
in none Humanities (NHU) and assignment of teaching duties (n = 240).
Class A B C D E F
HU 84.9 56.8 46.5 55.7 44.9 44.4
NHU 67.7 43.2 41.5 38.2 36.3 31.4
Treatment C E C E C E
Academic StaffA B B B A A
Copyright © 2012 SciRes.
Research Des ign
Table 2.
Non-equivalent control group design.
Pre-entry Characteristic Treatment Outcomes
HU English sc ores Mastery le arning English scores
NHU English scores Conventional Classification
LPQ deciles scores Learning approach Attitude
In order to the implementation of ML procedures, the learn-
ing materials were divided into smaller teaching units to be co-
vered within five days of the teaching and learning time. Students
learned the subject matter in a class with about 35 students per
academic staff in three different classes. The instruction on each
teaching unit was administered in a 4-phases including initial
instruction, formative test A, c orrective instruction and formative
te st B. The initial instruction was similar to those in the conven-
ti onal non-mastery classes. After the teaching, assignments were
given to students of all classes.
During the next double period a short formative test that care-
fully assessed ML objectives was given. It was usually in the
form of a short quiz covering the materials learned in a particu-
lar teaching unit. The test was criterion referenced and was not
co unted in the fi nal gra de. The te st wa s given ap proxi mately once
per cycle for the purpose of feedback typically taking about 15
minute s to complete and was marked by the subject teacher con-
cerned and returned to students in the next class session. These
tests were mainly used to diagnose the learning a weakness of
students so that both the academic staffs and students can get
immediate feedback to improve their learning activates.
The students who did not attain 70% ML standard were given
corrective exercises to be done outside class time. Those who
had demonstrated ML were given times which included instruct-
ing their classmates who needed corrective activities. After the
corrective exercise a parallel formative test was given to the non
masters to check their progress The parallel formative test was
given two or three days after the first one. Also, the test scores
of the control and experimental groups of learners with differ-
ent learning approaches were calculated. A repeated measure
two ways ANOVA with approaches x test time was performed
on these means.
In the first time, the results on the summative test were ex-
amined. The ANCOVA indicated that bot h approaches and treat-
ment had significant main effects on the scores (F = 3.33, P <
0.05, F = 5.06, P < 0.05), as did their interaction (F = 3.22, P <
0.05). The treatment main effect appears to support previous
research findings that ML and teaching process does have a po-
sitive effect on learning (Davis and Sorrell 1995) but the inter-
action shows that this is mainly limited to surface students (Ta-
ble 3).
Table 3 shows that those who had a preferred surface learn-
ing, appeared to do considerably better in the ML.
However, these data do not show how students with different
preferred approaches to learning might react from test time to
Table 3.
Mean scores of groups o f st ud e n ts on tests.
Control experimental difference
Surface Learner (SL)37.42 (26) 53.41 (37) +15.99
No bias 63.22 (12) 54.33 (29) –8.89
Deep Learners (DL) 62.35 (42) 68.69 (53) +6.34
Total 65.38 (71) 60.80 (8 5) +4.58
time within the mastery treatment. Accordingly it was decided
to use a repeated measure ANOVA with tests 1 to 4 as the de-
pendent variables and preferred approach as the independent
variable. There were no significant main effects for approach or
test occasions but a significant approach x test occasions inter-
action (F = 7.17, P < 0.01).
Students from the control group were told about the nature of
ML and asked how they thought they would like it. Deep learn-
ers from the control group thought that mastery retesting would
require students to attend to the tests in a different way and this
would be a positive challenge while the surface learners expres-
sed dislike for the viewpoints of continual resetting.
Therefore, it can be concluding that the ML does have a po-
sitive effect on surface learners which is cognate with the find-
ings by Kulik et al. (1990) that mastery learner especially bene-
fits those of average or low ability. However, the present results
show that ML is preferred by surface learners and indeed it is
likely that it promotes surface learning and has little or no
benefit in terms of improving the cognitive skills and analytical
power of students.
Conclusion and Discussion
Previous studies had confirmed positive effects of the ML and
teaching on student achievement: general achievement, specific
achievement by grade level and subject area knowledge reten-
tion time on task and learning rate (Davis & Sorrell, 1995; Gus-
key, 2007; Zimmerman & Dibenedetto, 2008)). However, these
studies have not investigated the effects of the ML on cognitive
and analytic skills and on students study approaches.
This paper looks at the effects of ML and teaching on students
study approaches and cognitive skills. Results showed that over
repeated trials deep and surface learners, increasingly diverge
surface learners doing better each trial and deep worse. On the
other hand, surface students of low ability seem to be motivated
to study as they are given more chances to secure a pass.
The results of this study suggest that students who perceive
they think and act to ML are more likely to report greater use of
deep learning strategies. Also, students who have a negative view
of the value of learning will report less use of surface learning
While there is much research to suggest that engagement is a
way to help students learn, the findings of this study show that
cou rse value has a stronger correlation with a deep learning stra-
tegy than engagement does. Our measure of ML is consistent
with Hulleman (2007), where the course is perceived as useful
or important to other tasks or aspects of an individual’s life.
The important role of ML in this study suggests that it would
be fruitful for future research to examine how to enhance the
ML to the student. Activities that are seen as useful have been
Copyright © 2012 SciRes. 43
Copyright © 2012 SciRes.
described as relevant to the student’s life or future. Use of parti-
cipation, interaction, deep learning may help in showing rele-
vance, but further research is needed to show these connections
between classroom tasks and ML.Finally , surface learning stra-
tegies will delay ML while deep learning strategies help to ML
In fact in Surface learning, the student is simply trying to pass
the course with minimal effort.
Biggs, J. B. (1987). Student approaches to learning and studying. Haw-
thorn, Victoria: Australian Council for Educational Research.
Block, J. H., & Anderson, L. W. (1975). Mastery learning in classroom
instruction. New York: Macmillan.
Bloom, B. S. (1976). Human characteristics and school learning. New
York: McGraw-hill.
Bloom, B. S. (1981). Human characteristics and school learning. New
York: McGraw-hill.
Cano, F. (2007). Approaches to learning and study or chestrations in
high school students. European Journal of Psychology of Education,
22, 131-151. doi:10.1007/BF03173518
Cohen, S. A. (1977). Instructional systems in reading: A report on the ef-
fects of a curriculum design based on systems model. Reading World,
16, 158-171. doi:10.1080/19388077709557358
Cole, N. (1990). Conceptions of educational achievement. Educational
Researcher, 19, 2-7.
Corno, L., & Mandinach, E. B. (1983). The role of cognitive engage-
ment in classroom learning and motivation. Educational Psycholo-
gist, 18, 88-108. doi:10.1080/00461528309529266
Dense D., & Sorrell, J. (1995). Mastery learning in public schools.
Paper prepared for PSY 702: Conditions of learning. Valdosta, GA:
Valdosta State University
Guskey, T. R. (1985). Implementing mastery learning. Belmont, CA: Wads
Gus key, T. R. (2007). Closing achievement gaps: Revisiting Benja-
min’s. Bloom’s learning for mastery. Journal of Advanced Academ-
ics, 19, 8-31.
Hidi, S., & Renninger, K. A. (2006). The four-phase model of interest
development. Educational Psychologist, 41, 111-127.
Hulleman, C. S. (2007) The role of utility value in the development of
interest and achievement. Madison, WI: University of Wisconsin-
Madison, Unpublished Doctoral Dissertati on .
Kulik, C. L. C., Kulik, J. A., & Bangert-Drowns, J. (1990). Effective-
ness of mastery learning programs: A meta analysis. Review of edu-
cational research, 60, 265-299.
Marsh, H. W., & Roche, L. A. (2000). Effects of grading leniency and
low workload on students’ evaluations of teaching: Popular myth,
bias, validity, or innocent bystanders? Journal of Educational Psy-
chology, 92, 202-2 28. doi:10.1037/0022-0663.92.1.202
Robinson, C., & Hullinger, H. (2008). New benchmarks in higher edu-
cation: Student engagement in online learning. Journal of Education
for Business, 84, 101-109. doi:10.3200/JOEB.84.2.101-109
Tagg, J. (2003). Th e learning paradig m college. Boston, MA: Anker.
Zimmerman, B. J., & Dibenedetto, M. K. (2008). Mastery learning and
assessment: Implications for students and teachers in an era of high
stakes-testing. Psychology in the Schools, 43, 206-216.