Many researches study the presence of a significant cluster of faculty based on the results of student and supervisors’ evaluation. However, there are very few researches that try to determine if the profile of the faculty (sex, age, rank, length of years teaching and salary) and their ratings on Evaluation of Teachers by Students (ETS) and College Evaluation of Teachers by Administrators (CETA) significantly discriminate significant cluster.
Based on the ETS and the CETA result, this research, through the hierarchal cluster analysis, shows two (2) groups of faculty, the very good and the exceptional. The variance analysis (ANOVA) confirms that significant differences exist in the ETS and CETA ratings between two clusters. Finally, the discriminant analysis method illustrates that only the ETS rating significantly discriminate between the very good and exceptional faculty.
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Contents
Abstract ... 1
Introduction ... 3
Method ... 4
Statistical Treatment of Data ... 4
Two Cluster Analyses of Faculty Members at the CBAE ... 6
Discriminant Analysis between the Very Good and Exceptional Group ... 6
Results and Discussions ... 7
Conclusions ... 9
Recommendations ... 9
Cited Literature ... 10
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Introduction
Most tertiary schools use the ratings result of the Evaluation of Teachers by Students (ETS) for several
academic reasons. The practice of one university is to gather the ETS ratings of the faculty members, four times
in an academic year, one in each term, for all courses. By asking students to regularly evaluate their faculty, it
sends a strong message that the college is serious in measuring the faculty teaching effectiveness (Onwuegbuzie
et al., 2007). Perhaps, the most important benefit the ETS results provide the faculty are the students' feedback.
Some feedbacks help the faculty adopt measures and techniques to refine their course and teaching practices and
provide students better learning experiences (Diseth, 2007). Giving importance on teaching methods, the student
evaluation provides a positive teaching and learning environment at the college. Immediately, student evaluation
results confirm to instructors the teaching techniques suitable to their student learning and suggest areas for
improvement (Berk, 2005). These suggestions might be area faculty members are not familiar, which they can
immediately make significant changes (Feldman, 2007).
Meanwhile, most researches focused on evaluation of teachers by students very few studies tried to take
the administrators' perspective in the evaluation of teachers. Admittedly, more researches are needed to study the
administrator's perspective in the evaluation of the teacher's classroom performance. Some teachers even believe
that administrators lack the necessary expertise to competently evaluate them inside the classroom (Posavac,
2015). In an interview of 115 teachers, they said administrators rarely offered ideas for improvement. They
believed that the rating forms and items are too narrow; almost driving administrators to be picky in their criticism
in order to look discriminating during their classroom evaluation of teachers (CochranSmith et al., 2009).
Nevertheless, the lack of administrator's competence to evaluate is the major source of dissatisfaction among
teachers. Administrators often perceived classroom evaluation of teachers is something they need to do rather than
something they want to do (Leithwood & Beatty, 2007).
This research aims to cluster between the exceptional and very good teachers then examine how
significant are the Evaluation of Teachers by Students (ETS), Classroom Evaluation of Teachers by
Administrators (CETA), the age of teachers, salary level and number of years teaching in distinguishing the
exceptional from very good teachers. In doing so, allow policy makers to understand the extent which teacher's
profile and the evaluation of teachers by administrators or students helped in distinguishing the exceptional from
the very good teachers.
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Method
There were 46 faculty members from College of Business Administration in the population. The study
used the descriptive study. In order to extract a deeper meaning from the profile and result of the ETS and CETA,
the study used the multivariate techniques of hierarchal cluster analysis to determine if significant groupings of
faculty exist. Furthermore, to distinguish the possible groupings of faculty in terms of their employment profile,
ETS and CETA ratings, the discriminant analysis method was used.
Statistical Treatment of Data
In Table 1, the age distribution of the faculty members at the CBAE depicted below demonstrate that
most of them, about 20 or 44 percent have ages 55 years old and above. There were both 7 and 15 percent of the
faculty whose ages are 36 to 41 and between 42 to 47 years old. About 20 or 43 percent of faculty members have
2 to 6 years of stay in college, while 7 or 15 percent has 19 to 23 years. In terms of salary, most of the faculty 31
or 67 percent receives the salary of 145 to 178 per hour, while, 7 or 15 percent receives 179 to 212 per hour.
The evaluation of teachers by students (ETS) in Table 2, demonstrate that 19 or 41 percent of the faculty
receive a rating between 4.30 to 4.50, described as very good, while faculty members that receives a rating of 4.10
to 4.20 (good) and 4.60 to 5.00 (outstanding) have both nine or 20 percent each. There were 18 or 39 percent of
the faculty members receives a rating between 4.50 to 4.70, described as very good, while 14 or 30 percent receives
outstanding.
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Table 1. Descriptive Profile of CBAE Faculty
Age
Frequency
percentage distribution
23 to 28
1
2%
29 to 35
6
13%
36 to 41
7
15%
42 to 47
7
15%
48 to 54
5
11%
55& above
20
43%
Years
one and below years
1
2%
2 to 6
20
43%
7 to 12
4
9%
13 to 18
5
11%
19 to 23
7
15%
24 to 29
5
11%
30 & above
4
9%
Salary
144 & below
3
7%
145 to 178
31
67%
179 to 212
7
15%
213 to 245
3
7%
246 to 281
1
2%
282 & above
1
2%
Table 2. Descriptive Distribution of CBAE ETS and CETA
ETS
Frequency
percentage
3.10 & below
1
2%
3.11 to 3.4
1
2%
3.5 to 3.70
2
4%
3.80 to 3.90
5
11%
4.10 to 4.20
9
20%
4.30 to 4.50
19
41%
4.60 to 5.00
9
20%
CETA
3.10 & below
1
2%
3.80 to 4.00
3
7%
4.10 to 4.40
10
22%
4.50 to 4.70
18
39%
4.80 to 5.00
14
30%
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Two Cluster Analyses of Faculty Members at the CBAE
The hierarchical cluster analysis method, demonstrate, two significant clusters of faculty. Using the ETS
and CETA ratings, there were two groups of faculty members in both the ETS and CETA rating. In the ETS and
CETA, the two significant groups were the very good which have 37 members or 80 percent and the exceptional
with nine (9) members or 20 percent. In the CETA, the very good group has earned a rating of very good (M =
4.43, SD = .368) while the exceptional group earned outstanding ratings (M = 4.80, S.D. = .224). Surprisingly,
the ETS rating of the exceptional group (M = 3.67, S.D. = .303) is lower but with the same rating of very good
with the very good group (M = 31, S.D. = .218). In order to distinguish if a significant difference exists between
the very good and the exceptional group, the study used analysis of variance (ANOVA). The CETA result,
demonstrates that the significant difference existed [F(2,46) = 8.40, p = .006), revealing that the exceptional group
has a significant higher CETA ratings than the very good group. Similarly, the result for the ETS shows that the
significant difference existed [F(2, 46) = 53.62, p = 0] with the very good group having the significant higher ETS
rating than the exceptional group.
Table 3. ANOVA between Very Good and Exceptional group
p<0.05
Discriminant Analysis between the Very Good and Exceptional Group
The result of discriminant analysis reveal that in the test of equality of the group means the result of the
univariate ANOVA's, carried out for each independent variable present that the CETA (.006), ETS (0) and sex
(0) of the faculty different between the very good and the exceptional group. The eigenvalue (4.324, 100, .901)
indicates the proportion of variance explained which demonstrate the very high canonical correlation (.901) is an
indication that the function discriminates well near 1.00 which is perfect. The Wilk's Lambda (.188, 0)
demonstrates the ratio of within-group sum of squares to the proportion of the total variance in the discriminant
score is significant. Meanwhile, the structure matrix shows that only the ETS (.531) significantly distinguished
ANOVA
1.007
1
1.007
8.403
.006
5.273
44
.120
6.280
45
2.976
1
2.976
53.623
.000
2.442
44
5.549E-02
5.417
45
Between Groups
Within Groups
Total
Between Groups
Within Groups
Total
CETA
ETS
Sum of
Squares
df
Mean Square
F
Sig.
7
between the very good and exceptional group of faculty. Lastly, the classification result indicates that 100 percent
of the original group cases were correctly classified.
Results and Discussions
The result of the student evaluation (ETS) remains to be the most significant instrument that distinguishes
the very good from the exceptional faculty. Studies observed that exceptional teachers immediately revise their
teaching methods upon the request of students. For instance, they can provide more clarity in their lectures by
providing more concrete and real world examples (Borman & Kimball, 2005). These teachers provide a more
clear connection between assignment, lectures and exams. By all accounts, exceptional faculty listens to their
colleagues and seeks help of a specialist in order to interpret the student's comments on their evaluation (Tucker
& Stronge, 2005).
Setting realistic course objectives
Student evaluation facilitates change. An exceptional teacher tries to reflect on the belief and goals of
course. In other words, the teacher delves into improving the teaching and learning of student in the course (Tucker
& Stronge, 2005). For instance, planning the course does not involve filling in the time, but setting course
objectives that bring students to learn more. In the process, the teacher starts with knowing the level of student's
comprehension, which bridges the gap between teaching and learning (Stronge & Tucker, 2003).
Planning or setting course objectives in congruence with the effective teaching strategy in reference to
the level of class comprehension involves reflection. These reflections help the exceptional teacher put student
comments on perspective (Peterson, 2004). Contextualizing these comments is a process of levelling off between
the teacher and students. The result is an appropriate teacher response on the student reaction on the course
(Kimball, White, Milanowski, & Borman, 2004).
Identifying strengths and weaknesses
One of the most important considerations among the exceptional teacher is to identify their strength.
Strengths are the specific teaching behaviors that led to high ETS rating. These aspects of teaching behavior are
important because the student directly inform the college of elements that are significantly to learning. At the
same time, identifying weaknesses, areas with low ratings, allow the department to identify the reasons that lead
to low ETS ratings (Davidovitch & Soen, 2006).
8
Experts suggest that after the teacher receives the result of the student evaluation, they must target one
or two items for improvement in their next class. Together with assistance of their program heads, they can select
teaching strategies that can improve student learning. The faculty must keep an open mind on what students are
trying to say (Dunrong & Fan, 2009). After the faculty received the feedbacks, it takes time to observe a significant
change in the target teaching strategy (Helterbran, 2008).
The purpose of student evaluation is to improve the teaching effectiveness of the faculty. In the end, the
students greatly benefit from changes. Improvements in teaching strategy enable students to learn more effectively
and efficiently. It is a good practice to disseminate feedbacks earlier.
Reading between the Lines of Student Written Comments
It is natural among the faculty to encounter difficulty on understanding the student's written feedbacks.
In most instances, these comments usually abound with a wide spectrum of observations and insights (Clayson &
Sheffet, 2006). Obviously, the faculty encounters difficulty in drawing conclusion from them. The faculty initial
reaction is that the student's comments appear to be contradictory where some students claim one thing while
others observe the opposite. Naturally, it results in frustration among the faculty and later make them dismiss the
important message that students provide (Rovai, Ponton, Derrick, & Davis, 2006).
Instead of motivating the faculty to improve their teaching strategies, written comments of students often
demoralized them. Lewis, (2010) states that the common practice among schools is to lump together in the
unorganized or unsystematic manner a wide spectrum of students' comments and the faculty read these from top
to the bottom. Setting a system in delivering student comments that group statements according to the quantitative
rating can provide context to the written comments of students. For instance, the faculty tends to dismiss student
comments to improve teaching strategies if the comment comes from both students who gave an excellent and
poor rating.
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Conclusions
Student evaluation (ETS) alone can distinguish between the very good and exceptional teachers.
Although, generally, ETS ratings of both groups are described as very good, more teachers were under the very
good ratings. However, teachers who earned the outstanding rating in CETA and were grouped under the
exceptional have lower ETS ratings.
Recommendations
Although the practice of the university is to sort and put together similar comments which results in two
(2) basic categories of strengths and weaknesses (Back, 2012), a better practice is to list the most frequent
comment with its corresponding frequency. In this manner, the faculty has a better perspective to analyze
systematically students' comments (Ellis, 2014). Later, the program head conferred with the faculty to identify
the areas where they can make most improvements (Feldman, 2007). An additional dimension in improving the
ETS is for the program heads to choose five characteristics of effective teaching and the teacher to list on the
adjacent right column comments based on student ratings. The filled chart may show patterns that reveal concern
of students who rated the teacher higher and needs of students who rates lower (Stronge, 2005).
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Cited Literature
Back, W. (2012). Center for Teaching and Learning.
Berk, R. A. (2005). Survey of 12 strategies to measure teaching effectiveness. International Journal of Teaching
and Learning in Higher Education, 17(1), 48-62.
Borman, G. D., & Kimball, S. M. (2005). Teacher quality and educational equality: Do teachers with higher
standardsâ based evaluation ratings close student achievement gaps? The Elementary School
Journal, 106(1), 3-20.
Clayson, D. E., & Sheffet, M. J. (2006). Personality and the student evaluation of teaching. Journal of Marketing
Education, 28(2), 149-160.
Cochran-Smith, M., Shakman, K., Jong, C., Terrell, D. G., Barnatt, J., & McQuillan, P. (2009). Good and just
teaching: The case for social justice in teacher education. American Journal of Education, 115(3), 347-
377.
Davidovitch, N., & Soen, D. (2006). Class attendance and students' evaluation of their college instructors.
College Student Journal, 40(3), 691.
Diseth, Ã. g. (2007). Students' evaluation of teaching, approaches to learning, and academic achievement.
Scandinavian Journal of Educational Research, 51(2), 185-204.
Dunrong, B., & Fan, M. (2009). On student evaluation of teaching and improvement of the teaching quality
assurance system at higher education institutions. Chinese Education & Society, 42(2), 100-115.
Ellis, R. (2014). Quality Assurance for University Teaching.
Feldman, K. A. (2007). Identifying Exemplary Teachers and Teaching: Evidence from Student Ratings1 The
scholarship of teaching and learning in higher education: An evidence-based perspective (pp. 93-143):
Springer.
Helterbran, V. R. (2008). The ideal professor: Student perceptions of effective instructor practices, attitudes,
and skills. Education, 129(1), 125.
Kimball, S. M., White, B., Milanowski, A. T., & Borman, G. (2004). Examining the relationship between teacher
evaluation and student assessment results in Washoe County. Peabody Journal of Education, 79(4),
54-78.
Leithwood, K., & Beatty, B. (2007). Leading with teacher emotions in mind: Corwin Press.
Lewis, K. G. (2010). Pathways toward improving teaching and learning in higher education: International
context and background. New Directions for Teaching and Learning, 2010(122), 13-23.
Onwuegbuzie, A. J., Witcher, A. E., Collins, K. M. T., Filer, J. D., Wiedmaier, C. D., & Moore, C. W. (2007).
StudentsâTM perceptions of characteristics of effective college teachers: A validity study of a teaching
evaluation form using a mixed-methods analysis. American Educational Research Journal, 44(1), 113-
160.
Peterson, K. (2004). Research on school teacher evaluation. NASSP Bulletin, 88(639), 60-79.
Posavac, E. (2015). Program evaluation: Methods and case studies: Routledge.
Rovai, A. P., Ponton, M. K., Derrick, M. G., & Davis, J. M. (2006). Student evaluation of teaching in the virtual
and traditional classrooms: A comparative analysis. The Internet and higher education, 9(1), 23-35.
Stronge, J. H. (2005). Evaluating teaching: A guide to current thinking and best practice: Corwin Press.
Stronge, J. H., & Tucker, P. D. (2003). Handbook on teacher evaluation: Assessing and improving performance:
Eye on Education Larchmont, NY.
Tucker, P. D., & Stronge, J. H. (2005). Linking Teacher Evaluation and Student Learning: ERIC.
Frequently asked questions
What is the main topic of this document?
This document presents an analysis of faculty member evaluations at the College of Business Administration and Economics (CBAE). It explores the differences between "very good" and "exceptional" teachers based on student evaluations (ETS), administrator evaluations (CETA), and faculty profiles (age, salary, years teaching).
What is the purpose of the research?
The research aims to cluster the "exceptional" and "very good" teachers based on evaluation data. It seeks to determine the significance of various factors (ETS, CETA, age, salary, teaching experience) in distinguishing between these two groups, potentially informing policy decisions about teacher evaluation and development.
What methods are used in this study?
The study utilizes a descriptive approach. Hierarchical cluster analysis is used to identify significant groupings of faculty members. Discriminant analysis is then employed to determine which factors (ETS, CETA, demographic variables) best distinguish between the identified faculty groupings.
What are some of the key findings of this study?
The initial findings indicate that student evaluations (ETS) appear to be a significant factor in distinguishing between "very good" and "exceptional" faculty members. There are two clusters of faculty, the "Very Good" group and the "Exceptional" group, as rated by ETS and CETA.
What data is collected from faculty for this study?
The study collects age, number of years teaching and the salary levels of CBAE faculty. It also collects both student ratings and ratings from administrators.
What are some recommendations mentioned in the document?
The document recommends presenting student feedback with corresponding frequencies to allow faculty to better understand the comments. It also suggests program heads should work with faculty to identify areas for improvement, and that there should be characteristics of teaching that program heads select for faculty improvement.
What is ETS and CETA?
ETS refers to the Evaluation of Teachers by Students. CETA refers to Classroom Evaluation of Teachers by Administrators.
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