Content uploaded by Elenwo Pritta Menyechi
Author content
All content in this area was uploaded by Elenwo Pritta Menyechi on Sep 27, 2023
Content may be subject to copyright.
African Scholars Multidisciplinary Journal (ASMJ), Vol.4, August 2023. Pg.150 - 158
150
PERSONAL CHARACTERISTICS OF STUDENTS AS PREDICTORS OF EDUCATIONAL
MANAGEMENT POST GRADUATE STUDENTS’ ACADEMIC PERFORMANCE IN
RESEARCH AND STATISTICS IN RIVERS STATE UNIVERSITY
ELENWO PRITTA MENYECHI (PhD) & EBOM-JEBOSE ABIGAIL (PhD)
Email: prittam@yahoo.com & abigailego001@gmail.com
Department of Educational Management
Faculty of Education, Rivers State University
ABSTRACT
This study examined personal characteristics of students as predictors of Educational Management post
graduate students’ academic performance in Research and Statistics in Rivers State University. Three
research questions and three hypotheses were formulated to guide the study. A simple prediction research
design was used for the purpose of the study. The population of the study comprised 38 Ph.D. Educational
Management students of 2020-2021 and 2021-2022 academic sessions in Rivers State University. The entire
population of 38 Ph.D. Educational Management students were studied as an intact class without sampling.
Two instruments were designed and used to address the three research questions of the study. These were
the “Personal Characteristics of Students Questionnaire” and “Research and Statistics Achievement Test”.
The instruments were subjected to face and content validity by three experts, one expert in Educational
Research, one in Statistics and another in Measurement and Evaluation. The reliability of the instruments
was established using Cronbach Alpha reliability coefficient while the reliability coefficients of both
instruments stood at 0.80 and 0.78 respectively. The data collected were analyzed using Simple Linear
Correlation Statistical Tool at .05 alpha level of significance. The result showed that students’ learning
styles, students’ class attendance rate and students intelligence levels predicted Educational Management
post graduate students’ academic performance in Research and Statistics significantly. Consequently, it was
recommended that teachers should be innovative with the teaching method employed during teaching in
order to ignite a spark of energy amongst the students being taught and make learning of the subject
irresistible, teachers should make class attendance mandatory and allocate marks to it as a strategy to
increase attendance rate of students in their classes and teachers should observe their students closely in
order to identify the ones with low level of intelligence and ensure they render the needed specialized help
to them.
Keywords: Students characteristics, Predictors, Post graduate students, Academic performance, Research,
Statistics
Introduction
University education is an education given after a successful completion of secondary education in tertiary
institutions of learning. It includes education given in colleges of education, polytechnics, monotechnic and
other institutions offering correspondence courses. The Federal Government of Nigeria (FGN, 2014) in its
National Policy on Education enumerated the goals of the university to include: contribute to national
development through high level relevant manpower training; develop and inculcate proper values for the
survival of the individual and society, develop the intellectual capability of individuals to understand and
appreciate their local and external environments etcetera. The policy further stated that the enumerated goals
shall be pursued through but not limited to teaching; research and development.
Research is discovery that is used to unearth or establish hidden phenomena. It is a scientific process through
which unknown facts are found and used for the benefit of man. Akaninwor (2014) opined that research is
the function of disciplined individuals. In order words, undisciplined individuals can hardly be involved in
serious research efforts. The university is focused on producing disciplined and high-quality manpower
hence, the requirement for compulsory conduct of research among its teaching staff and students at the
undergraduate and post graduate levels to enable their promotion and graduation respectively. Research
African Scholars Multidisciplinary Journal (ASMJ), Vol.4, August 2023. Pg.150 - 158
151
requires gathering, analyzing and interpretation of collated data by researchers thus, it became imperative
for every university student especially, the post graduate students to possess a good knowledge of statistics.
Statistics is the study of the collection, organization, analysis, interpretation and presentation of data. It is a
branch of Mathematics that has to do with numbers. Statistics can be used to predict results or outcomes in
events or phenomena. In other words, Statistics can be used to predict the conditions affecting an event or
phenomena with a high degree of accuracy. As a tool for scientific investigation, Statistics helps the modern
scientist to design experiments, avoid or minimize bias and arrive at testable and verifiable conclusions on
scientific events or activities (Akaninwor, 2014). Specifically, Statistics studied tends to interpret result or
data. Considering its characteristics and importance, Statistics is a standard course of instruction at all levels
of the university educational system, from undergraduate to post graduate levels. Research and Statistics
play vital roles in the quality of students’ project, dissertation, thesis, and so on hence taught from the
graduate level to the post graduate level in all universities. Given the nature of Research and Statistics and
the differences in personal characteristics of students such as learning styles, class attendance rate and
intelligence levels, it was observed that students perceive the courses as difficult and developed phobia which
results in hiring mercenaries who helped in carrying out their research, analyze the data collated from the
field and write their examinations. Student characteristics is a personal quality of students who become
characteristic and indicate the condition of students. This individual characteristic is believed to be a special
ability that influences the degree of success in following a program (Barringer, Pohlman & Robinson, 2010).
Learning style is defined as the characteristics, strengths, and tendencies that a person has towards how to
receive and process information (Felder & Silverman, 2018), and is related to the way a person begins to
concentrate on processing and storing new and difficult information (Dunn, & Griggs, 2018). According to
Mehlenbacher (2010) learning styles are easier to alter over time with instruction, motivation, trial and error,
and experience. Weli and Elenwo (2020) found that students learning styles influence their performance in
Mathematics in Junior Secondary Schools in Rivers State to a high extent.
Class attendance is referred to as the physical presence of a student in the class as witnessed in a face-to-
face class and students involvement taking place with the timelines as imposed by the course lecturer in the
case of on-line class (Carbonaro, 2015). A learner’s regular attendance at school greatly contributes to his/her
academic performance. Poor school attendance no matter the reason reduces the quantity of instruction a
learner is expected to receive from the classroom as well as his/ her understanding of major concepts in the
syllabus. Class attendance enhances learning, on average; students who attended most classes made the
highest grades, despite the fact that they received no points for coming to class Moore (2016). Also, Ogweno,
Kathuri and Obara (2014) revealed that students who did not miss classes had a higher mean score as
compared to those who sometimes missed classes and that students who regularly attend classes perform
better than those who miss classes. In the view of Carbonaro (2015) class attendance is influenced by the
learners’ perception about school, which is a significant determinant of academic achievement. Therefore, it
is incumbent upon teachers and parents to encourage learners to develop interest in school.
Intelligence levels can be described as the ability to solve problems or to create products, that are valued
within one or more cultural settings (Gardner, 2011). Gardner (2016) opined that whether and in what
respects an individual may be deemed intelligent is a product of his genetic heritage and his psychological
properties, ranging from his cognitive powers to his personality dispositions. The level of intelligence of
students can affect students' confidence in facing difficult material (Scheiter, Gerjets, Vollmann
&Catrambone, 2009). All students have various intelligence, or ways of receiving and expressing their
knowledge. These intelligences can serve as a powerful springboard for creative curricular decision making
and instructional planning (Ferguson,2011). Accordingly, Hidi (2016) asserts that a child with intellectual
deficiencies tends to grieve in school because he or she is measured, compared with others and experience
humiliating insults from peers. Furthermore, combination of intelligence quotient and other factors such as
socio-economic status, learning resources and time management influences one’s academic performance and
achievement.
Academic performance is the extent to which a student has attained his short or long-term educational goals.
It refers to different levels of quantifiable and apparent behaviour of learners after an instructional process.
African Scholars Multidisciplinary Journal (ASMJ), Vol.4, August 2023. Pg.150 - 158
152
Factors that influence students' academic performance are not conclusively known and could be multivariate
in nature. However, Udoh (2012) maintained that academic performance is occasioned by a combination of
several social, economic, psychological cum environmental factors.
Statement of the Problem
Research and Statistics are compulsory courses for university students and most especially in the post
graduate programme which is research-based. This is to achieve the goal of training high level manpower
who are knowledgeable and can independently conduct research in their fields of study and competently
analyze the data collected thereon. Regrettably, students including Educational Management post graduate
students perform poorly in these courses in examination, conducting of research and analyzing generated
data from the field. This validates the report of Stansfeld and Matheson (2013) that the quality of research
being carried out in Nigerian academics has been adjudged to be low standard when compared to their
counterparts in other parts of the world. It also confirms Maruff and Amos (2011) who noted that several
studies in the field of Statistics and Mathematics teaching indicate that students experience difficulties in
subjects related to concepts. In an attempt to bridge the gap, universities usually encourage students to
register for seminars, workshops, and conferences where papers are presented by students, and staff of
different organizations and vetted by experienced academics who have distinguished themselves in their
different areas of specialization. Irrespective of their efforts, students still find the two courses difficult. It is
against this backdrop that this study examined personal characteristics of students as predictors of
Educational Management Post Graduate Students’ academic performance in Research and Statistics in
Rivers State University.
Purpose of the Study
The purpose of this study was to investigate personal characteristics of students as predictors of Educational
Management post graduate students’ academic performance in Research and Statistics in Rivers State. The
study will specifically;
1. Determine the extent to which students’ learning styles predict Educational Management post
graduate students’ academic performance in Research and Statistics in Rivers State University.
2. Ascertain the extent to which students’ class attendance rate predict Educational Management post
graduate students’ academic performance in Research and Statistics in Rivers State University.
3. Examine the extent to which students’ intelligence levels predict Educational Management post
graduate students’ academic performance in Research and Statistics in Rivers State University.
Research Questions
The following research questions were raised to guide the study.
1. To what extent does students’ learning styles predict Educational Management post graduate
students’ academic performance in Research and Statistics in Rivers State University?
2. To what extent does students’ class attendance rate predict Educational Management post graduate
students’ academic performance in Research and Statistics in Rivers State University?
3. To what extent does students’ intelligence levels predict Educational Management post graduate
students’ academic performance in Research and Statistics in Rivers State University?
Hypotheses
The following null hypotheses were formulated for the study;
1. Students’ learning styles does not significantly predict Educational Management post graduate
students’ academic performance in Research and Statistics in Rivers State University.
2. Students’ class attendance rate does not significantly predict Educational Management post
graduate students’ academic performance in Research and Statistics in Rivers State University.
3. Students’ intelligence levels do not significantly predict Educational Management post graduate
students’ academic performance in Research and Statistics in Rivers State University.
African Scholars Multidisciplinary Journal (ASMJ), Vol.4, August 2023. Pg.150 - 158
153
Methodology
Simple prediction research design was adopted for the study. This was because the researchers investigated
how students learning styles, class attendance rate and intelligence levels when treated singly predict predict
Educational Management post graduate students’ academic performance in Research and Statistics. This
study was carried out in Rivers State University. The population of the study was made up of 38 Ph.D.
students of 2020-2021 and 2021-2022 academic sessions of the Department of Educational Management in
Rivers State University. The entire population of 38 Ph.D. students were studied as an intact class without
sampling. Two instruments were designed and used to address the three research questions of the study.
These are “Personal Characteristics of Students Questionnaire” (PCSQ) and the “Research and Statistics
Achievement Test” (RSAT). PSCQ had two sections; A and B. Section A contained items on the
demographic variables of the respondents. Section B had 15 items related to students’ learning style,
students’ class attendance rate and students’ intelligence levels. Each of the students’ characteristics had 5
items. Lastly, RSAT had 10 multiple choice question items with response options for each question. The
questions for RSAT were set based on the course outline for Research and Statistics for Ph.D. as specified
in the course descriptions of the courses in the departmental handbook. One mark each was awarded to every
item correctly responded. The total number of marks was ten (10). The instruments for PCSQ were scored
using a four-point rating scale of Very High Extent (VHE),
High Extent (HE), Low Extent (LE) and Very Low Extent (VLE) with assigned values of 4, 3, 2, and 1
respectively.
The instrument was subjected to face and validity by three experts, one expert in Educational Research, one
in Statistics and another Measurement and Evaluation. To determine the internal consistency of the
instrument, the researchers randomly selected 10 Ph.D. students in the Department of Educational
Management in the Faculty of Education, Ignatius Ajuru University of Education who were not part of the
study to respond to the instrument. Data generated was subjected to inter-item analysis using Cronbach
Alpha Reliability Coefficient Statistics. Cronbach Alpha Reliability Coefficients of PCSQ and RSAT were
0.80 and 0.78 respectively.
The researchers used one trained research assistant in administering the instruments. The rate of
questionnaires retrieved from the field work was 100 percent. Data collected were analyzed using Simple
Linear Regression Statistics in SPSS (Statistical Package for Social Science) Software Windows version 25.
Decision rule for the interpretation of the R-value was between 0 and 1 in Kpee (2003) gave the following
scale to determine the relationship and direction of the regression coefficient values. 0.00 – .19, Very weak
relationship (prediction), .20 - .39, Weak relationship (prediction), .40 – .59, Moderate relationship
(prediction). .60 – .79, Strong relationship (prediction), .80 – .1.0 Very strong relationship (relationship), 0,
No relationship (prediction), 1.0 and Perfect positive relationship (prediction).
Analysis of Data and Result/Hypotheses Testing
Research Question 1: To what extent does students’ learning styles predict Educational Management post
graduate students’ academic performance in Research and Statistics in Rivers State University?
Table 1: Simple Regression on the Extent Students’ Learning Styles Predict Educational
Management Post Graduate Students’ Academic Performance in Research and
Statistics in Rivers State University
Variables
R
R²
Extent of
Prediction
Adjusted R²
Remarks
Students’ Learning Styles
0.715
.511
51.1%
0.511
Strong
Prediction
Students’ Academic
Performance
African Scholars Multidisciplinary Journal (ASMJ), Vol.4, August 2023. Pg.150 - 158
154
In Table 1, the results reveal that R-value is 0.715 and R² is .511. The R- value of 0.715 indicates positive
and strong strength of prediction, while R² of 0.511 which is the coefficient of determination show the extent
of prediction of students’ learning styles on students’ academic performance in Research and Statistics. In
addition, 51.1% variance in students’ academic performance is predicted by students’ learning styles. This
means that there was a strong extent of prediction of students’ learning styles on Educational Management
Post Graduate students’ academic performance in Research and Statistics in Rivers State University.
Research Question 2: To what extent does students’ class attendance rate predict Educational Management
post graduate students’ academic performance in Research and Statistics in Rivers State University?
Table 2: Simple Regression on the Extent Students’ Class Attendance Rate Predict
Educational Management Post Graduate Students’ Academic Performance in
Research and Statistics in Rivers State University
Variables
R
R²
Extent of
Prediction
Adjusted R²
Remarks
Students’ Class
Attendance Rate
0.795
.632
63.2%
0.632
Strong Prediction
Students’ Academic
Performance
In Table 2, the results reveal that R-value is 0.795 and R² is .632. The R- value of 0.795 indicates positive
and strong strength of prediction, while R² of 0.632 which is the coefficient of determination show the extent
of prediction of students’ class attendance rate on students’ academic performance in Research and Statistics.
In addition, 63.2% variance in students’ academic performance is predicted by students’ class attendance
rate. This means that there was a strong extent of prediction of students’ class attendance rate on Educational
Management Post Graduate students’ academic performance in Research and Statistics in Rivers State
University.
Research Question 3: To what extent does students’ intelligence levels predict Educational Management
post graduate students’ academic performance in Research and Statistics in Rivers State University?
Table 3: Simple Regression on the Extent Students’ Intelligence Levels Predict Educational
Management Post Graduate Students’ Academic Performance in Research and
Statistics in Rivers State University
Variables
R
R²
Extent of
Prediction
Adjusted R²
Remarks
Students’ Intelligence
Levels
0.645
.416
41.6%
0.416
Strong Prediction
Students’ Academic
Performance
In Table 3, the results reveal that R-value is 0.645 and R² is .416. The R- value of 0.645 indicates positive
and strong strength of prediction while R² of .416 which is the coefficient of determination show the extent
African Scholars Multidisciplinary Journal (ASMJ), Vol.4, August 2023. Pg.150 - 158
155
of prediction of students’ intelligence levels and Educational Management Post Graduate students’ academic
performance in Research and Statistics in Rivers State University. In addition, 41.6% variance in students’
academic performance is predicted by students’ intelligence levels. This means that there was a strong extent
of prediction of students’ intelligence levels on Educational Management Post Graduate students’ academic
performance in Research and Statistics in Rivers State University.
Testing the Hypotheses
Hypothesis 1: Students’ learning styles does not significantly predict Educational Management post
graduate students’ academic performance in Research and Statistics in Rivers State University.
Table 4: Result of Regressing Academic Performance to Students’ Learning Styles (n=38)
Variables
Sources of
Variance
Sum of Square
Df
Ms
F-cal
F-crit
Decision n at
p< .05
Students’ learning styles
Regression
44894.32
1
44894.32
5.49
4.07
Significant
Students’ Academic
Performance
Residual
294185.54
36
8171.82
The result on Table 4 shows that the calculated F-value of 5.49 is greater than the critical F-value of 4.07 at
.05 level of significance with 1 and 36 degrees of freedom. With this result, the null hypothesis: students’
learning styles does not significantly predict Educational Management post graduate students’ academic
performance in Research and Statistics was rejected. This means that students’ learning styles does
significantly predict Educational Management post graduate students’ academic performance in Research
and Statistics in Rivers State University.
Hypothesis 2: Students’ class attendance rate does not significantly predict Educational Management post
graduate students’ academic performance in Research and Statistics in Rivers State University.
Table 5: Result of Regressing Academic Performance to Students’ Class Attendance Rate (n=38)
Variables
Sources of
Variance
Sum of
Square
Df
Ms
F-cal
F-crit
Decision n at
p< .05
Students’ Class
Attendance
Regression
930858.12
1
930858.12
9.88
4.07
Significant
Students’ Academic
Performance
Residual
3390446.92
36
94179.08
The result on Table 5 shows that the calculated F-value of 9.88 is greater than the critical F-value of 4.07 at
.05 level of significance with 1 and 36 degrees of freedom. With this result, the null hypothesis: students’
class attendance rate does not significantly predict Educational Management post graduate students’
academic performance in Research and Statistics was rejected. This infers that students’ class attendance
rate does significantly predict Educational Management post graduate students’ academic performance in
Research and Statistics in Rivers State University.
Hypothesis 3: Students’ intelligence levels does not significantly predict Educational Management post
graduate students’ academic performance in Research and Statistics in Rivers State University.
African Scholars Multidisciplinary Journal (ASMJ), Vol.4, August 2023. Pg.150 - 158
156
Table 6: Result of Regressing Academic Performance to Students’ Intelligence Levels (n=38)
Variables
Sources of
Variance
Sum of Square
Df
Ms
F-cal
F-crit
Decision n at
p< .05
Students’ Intellegence
Levels
Regression
76433.01
1
76433.01
9.28
4.07
Significant
Students’ Academic
Performance
Residual
296490.88
36
8235.86
The result on Table 6 shows that the calculated F-value of 9.28 is greater than the critical F-value of 4.07 at
.05 level of significance with 1 and 36 degrees of freedom. With this result, the null hypothesis: students’
intelligence levels do not significantly predict Educational Management post graduate students’ academic
performance in Research and Statistics was rejected. This concludes that students’ intelligence levels do
significantly predict Educational Management post graduate students’ academic performance in Research
and Statistics in Rivers State University.
Discussion of Findings
In hypothesis 1, students’ learning styles does not significantly predict Educational Management post
graduate students’ academic performance in Research and Statistics in Rivers State University. From the
results, the R-value of 0.715 indicates positive and strong strength prediction. With this result, the null
hypothesis that students’ learning styles does not significantly predict Educational Management post
graduate students’ academic performance was rejected. This means that students’ learning styles does
significantly predict Educational Management post graduate students’ academic performance. This result
was expected because students tend to learn and process information differently. This finding corroborates
the finding of Weli and Elenwo (2020) that students in Junior Secondary Schools in Rivers State learning
styles influence their performance in Mathematics to a high extent.
In hypothesis 2, students’ class attendance rate does not significantly predict Educational Management post
graduate students’ academic performance in Research and Statistics in Rivers State University. From the
results, the R-value of 0.795 indicates positive and strong strength prediction. With this result, the null
hypothesis that students’ class attendance rate does not significantly predict Educational Management post
graduate students’ academic performance was rejected. This means that students’ class attendance rate does
significantly predict Educational Management post graduate students’ academic performance. This result
was anticipated because class attendance allows students to obtain information that is not contained in
textbooks or lecture materials presented on-line but also allow students varied contact with materials
(lectures, review of notes, demonstrations and so on). This finding agrees with Ogweno, Kathuri and Obara
(2014) and Moore (2016) who stated that students who did not miss classes had a higher mean score as
compared to those who sometimes missed classes and that students who attended most classes made the
highest grades, despite the fact that they received no points for coming to class.
In hypothesis 3, students’ intelligence levels do not significantly predict Educational Management post
graduate students’ academic performance in Research and Statistics in Rivers State University. From the
results, the R-value of 0.645 indicates positive and strong strength prediction. With this result, the null
hypothesis that students’ intelligence levels do not significantly predict Educational Management post
graduate students’ academic performance was rejected. This means that students’ intelligence levels do
significantly predict Educational Management post graduate students’ academic performance. This result
was predictable because intelligence helps students to think, solve problems, analyze situations and
understand social values, customs and norms. The finding is in tandem with Hidi (2016) who asserts a
combination of intelligence quotient and other factors such as socio-economic status, learning resources and
time management influences one’s academic performance and achievement.
African Scholars Multidisciplinary Journal (ASMJ), Vol.4, August 2023. Pg.150 - 158
157
Conclusion
From the findings, it was concluded thus, personal characteristics of students such as students learning styles,
students class attendance rate and students’ intelligence levels significantly predict Educational Management
post graduate students’ academic performance in Research and Statistics in Rivers State University.
Recommendations
The following recommendations were made based on the findings of the study;
1. Teachers should be innovative with the teaching method employed during teaching in order to ignite
a spark of energy amongst the students being taught and make learning of the subject irresistible.
2. Teachers should make class attendance mandatory and allocate marks to it as a strategy to increase
attendance rate of students in their classes.
3. Teachers should observe their students closely in order to identify the ones with low level of
intelligence and ensure they render the needed specialized help to them.
References
Akaninwor, G. I. K. (2014). Research methods and statistics: Paradigms in education, science
and technology. Owerri: Civincs Publishers.
Barringer, M. D. Pohlman, C. & Robinson, M. (2010). Schools for all kinds of minds: Boosting
student success by embracing learning variation. San Fransisco: Jossey-Bass.
Carbonaro, W (2015). Tracking, students’ effort, and academic achievement. Sociology of
Education 78(1), 27-49.
Dunn, R. & Griggs, S. A. (2018). Multiculturalism and learning style. Westport: Praeger
Publisher.
Federal Republic of Nigeria (2014). National policy on education. Lagos: NERDC Press.
Felder, R. M. & Silverman, L. K. (2018). Learning and teaching styles in engineering education.
Eng. Education78(1), 674–681.
Ferguson, D. L. (2011). Designing personalized learning for every student. Virginia: ASCD.
Gardner, H. (2011). Frames of mind: The theory of multiple intelligences. New York: Basic
Books.
Gardner, H. (2016.) Multiple intelligences: New horizons. New York: Basic Books.
Hidi, S. (2016). Revisiting the role of rewards in motivation and learning: Implications of
neuroscientific research. Educational Psychology Review 28(1), 61-93.
Kpee, G. G. (2003). Teacher quality: Understanding the effectiveness of teacher attributes.
Washington, D.C.: Economic Policy Institute.
Marburger, D. R. (2011). Absenteeism and undergraduate exam performance. Journal of
Economic of Education, 32(2), 99-110.
Maruff, C. & Amos, A. (2011). Instructional materials and students’ academic achievement in
Mathematics: Some policy implication. European Journal of Humanities and Social
Sciences,1(2), 220-229.
African Scholars Multidisciplinary Journal (ASMJ), Vol.4, August 2023. Pg.150 - 158
158
Mehlenbacher, B. (2010). Instruction and technology: Designs for everyday learning.
Massachusetts: MIT Press.
Moore, R. (2016). Class attendance: How students’ attitudes about attendance relate to their
academic performance in introductory science classes. Journal of Research & Teaching in
Developmental Education, 23(1), 27-39.
Ogweno, P. O., Kathuri, N. N. & Obara, J. (2014). Influence of students’ characteristics on
academic performance in secondary agriculture, in Rachuonyo North Sub County, Kenya.
International Journal of Education and Research, 2(3), 1-12.
Scheiter, K. Gerjets, P. Vollmann, B. & Catrambone, R. (2009). The impact of learner
characteristics on information utilization strategies, cognitive load experienced, and
performance in hypermedia learning. Learning Instruction., 19(5), 387–401.
Stansfeld, Y. & Matheson, A. (2013). Learning environments research: Yesterday, today and
tomorrow. Singapore: World Scientific.
Udoh, A. O. (2012). Learning environment as correlates of chemistry students‘achievement in
secondary schools in Akwa Ibom State of Nigeria. International Multidisciplinary Journal,
Ethiopia, 6(3), 208-217.
Weli, S. E. & Elenwo, P. M. (2020). Behavioural factors influencing students’ performance in
mathematics in junior secondary schools in Rivers State. African Journal of Contemporary
Research and Development, 6(4b), 19-29.
