Relevance of Mastery Learning (ML) in Teaching of English (Case Study of the University of Guilan, Iran)

doi:10.4236/ce.2012.31007

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Creative Education

2012. Vol.3, No.1, 41-44

Published Online February 2012 in SciRes (http://www.SciRP.org/journal/ce) http://dx.doi.org/10.4236/ce.2012.31007

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

Email: [email protected]

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

mode.

Keywords: Learning; Mastery Learning; Surface Learning; Deep Learning

Introduction

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.

A. SADEGHI ET AL.

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

and;

2) To look at the effects of ML on the cognitive level of the

outcomes elicited.

Methodology

Participants

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

A. SADEGHI ET AL.

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

Treatment

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.

Results

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

strategies.

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

A. SADEGHI ET AL.

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.

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