Is this the Answer?
1
Physics Department
Is this the Answer?
2011
This report was supported by the Department of Physics. A graduate student was employed as an
observer in the Physics 160FC lectures in 2011 Semester 1. Funding was supplied to provide
professional development to the laboratory tutors for the Team-Based Learning and the Help-
Room tutors for student guidance during problem solving.
Thanks to Pearson Education for the opportunity to trial Mastering Physics, the on-line assessment
system that provided effective feedback to lecturers about questions students found difficult and
support to students through the problem solving structures.
Graham Foster
Physics Education Research
Room 303 .612 Physics Dept
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Abstract
The 2009 report by Graham Foster and Brett A Armstrong identified barriers to learning in
Physics at Stage 1 and suggested strategies that might improve students‟ view of, and confidence
about, studying Physics at Stage 1.
During 2010 several strategies were added to the mix in both Physics 120 „Physics of Energy‟ and
Physics 160 „Physics for the Biological Sciences‟. The use of clickers, together with in-lecture
problem solving, weekly bulletins, and Team Based Learning were some of the strategies
implemented.
In Semester1, 2011, these strategies were refined and applied. Additionally the opportunity was
provided by Pearson Education to trial Mastering Physics, an on-line assignment strategy that
had several advantages over the OASIS on-line system commonly used in Stage 1 Physics courses.
These strategies facilitated the transfer from „content-focused‟ teaching to the „student-focused‟
teaching based on socio-cultural theory and the work being promoted in the Carl Wieman Science
Education Initiative at the University of British Columbia, Canada.
This paper places the strategies used in the appropriate theoretical context and reports the
summative effect of implementing these strategies in the Physics 120 course. The student outcomes
in both on-course assessments and examinations is analysed to determine the relative effectiveness
of each strategy and the apparent gains that each strategy contributed to student performance.
Conclusions and recommendations are provided.
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Preliminary Contexts of this Report
The MoRST Report “Staying in Science 2” (2006) shows that Physics was the most disliked
subject of all the Sciences. While 52% studied Physics in Year 13, 40% of the sample expressed
dislike for Physics. Only 30% of students in the sample expressed a dislike for Chemistry and
19% expressed their dislike for Biology. The report showed that clear gender preferences in
science disciplines. Females were more likely to choose Biology, while males chose Physics.
The 2009 report to the Science Faculty and Physics Department:
How do we keep them? Identifying and dismantling barriers to participation, performance and
progression in Stage I Physics courses.
by Graham Foster, Physics Department, Faculty of Science, the University of Auckland and
Brett A Armstrong, Research Assistant, Sociology Dept, University of Auckland
identified barriers to learning in Physics at Stage 1 and suggested strategies that might improve students‟
view of and confidence about studying Physics at Stage 1.
In a the 2010 report to the Science Faculty and Physics Department
Student Perceptions of Studying Physics at the University of Auckland
Graham Foster (Senior Tutor), Yvette Wharton (SIT), Simon Kjellberg (PhD student)
reported on the steady decline of NCEA Level 3 entries in the Auckland Region, from 8215 in
2006 to 7527 in 2009. This report also provided information about the changes to the NCEA
awards in Level 2 and 3 through “endorsement‟ and possible effects of students coming to the
University of Auckland with a lower level of Physics appreciation since they may have attempted
fewer Achievement Standards. It detailed the hesitancy that students perceive about Physics and
the influences that caused students to study Physics at the University of Auckland.
Base Data of Enrolments 2007 to 2011
Table 1 provides data of student enrolments in Physics Stage 1 and 2 courses at the University of
Auckland Physics Department. There seems to be an increase in numbers in Stage 2 in 2010 and
2011. Some Stage 1 courses seem to be holding or slightly increasing their enrolment numbers,
other courses, such as Physics 102, seem to be showing significant, sharply reduced enrolments.
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Strategies and Objective for 2011
Since the end of 2009 the endeavour has been to change the teaching and learning processes in
Physics 120FC by implementing several strategies to encourage students to know that their
personal learning needs are being met more effectively through the strategies being used in
Physics. The strategies were intended to provide a greater sense of „belonging‟ and recognition,
while reducing cognitive load and student stress and increasing student confidence about their
ability to learn and achieve in Physics.
During 2010 several strategies were added to the mix in both Physics 120 „Physics of Energy‟ and
Physics 160 „Physics for the Biological Sciences‟. In Semester 1 2011 these strategies were
refined and implemented.
This paper reports on the strategies used to improve the students‟ view of Physics in the course
Physics 120. The outcomes from the previous research and reports, together with on-going
research readings, gave reasonably good definition to some of the strategies that should be used in
Physics 120 for 2011.
The objectives were:
1) To continue developing stronger relationships between students and the Physics Department
so that the Physics 120 FC course changes from „content-focused‟ to „student-focused‟.
2) To find ways to reduce the effects of any subject dislikes and lack of student confidence in
order to encourage students entering Physics 120FC to perform effectively, gain success
and to continue to Stage 2 Physics courses.
3) To determine if we have developed strategies to keep them!
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Physics Courses and Enrolments Course 2007 2007 2007 2008 2008 2008 2009 2009 2009 2010 2010 2010 2011
Local Int'l Total Local Int'l Total Local Int'l Total Local Int'l Total Total
102 323 42 365 278 42 320 272 35 307 240 21 261 300
107 136 22 158 126 8 134 182 20 202 164 17 181 344
107G 92 11 103 134 15 149 135 9 144 147 23 170
108
38 8 46 50 8 58 62 4 66 108
108G
24 7 31 27 6 33 28 6 34
111 21 1 22 27 1 28 30 3 33
120 296 35 331 280 35 315 293 23 316 279 28 307 308
130 57 16 73 46 10 56 120 5 125 63 5 68 109
140
88 9 97 107 12 119 77 14 91 135
150 189 26 215 169 23 192 192 23 215 201 27 228 227
160 623 65 688 654 49 703 652 58 710 689 53 742 730
210 18 1 19 16
16
211 18 2 20 27 1 28 14 1 15 33 3 36 39
213 25 6 31 21 7 28 22 11 33 22 4 26 37
220 44 1 45 35 10 45 42 4 46 69 15 84
230 35 4 39 35 13 48 41 6 47 58 6 64
231
76
240 48 4 52 47 9 56 34 7 41 48 9 57 61
243 17 1 18
250 44 3 47 43 8 51 33 1 34 50 2 52
251
69
260 29 3 32 24 4 28 15
15 36 6 42
261
68
270 20 3 23 29 3 32 16 1 17 23 4 27
280 17 2 19 11
11 12 1 13 9 1 10 16
Total Stage 1 1955
2071
2262
2148 2261
Total Stage 2 345
343
261
398 366
Table 1: Base Data from Faculty of Science providing detail of Stage 1 and Stage 2 enrolments 2007-11
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Introduction and Theory
Part 1: Retaining Information
Three separate surveys by Redish, Hrepic and Perkins have shown that students retain very little from a
lecture even if it only occurred 30 minutes previously. Students‟ short-term working memory is very
limited in its both its capacity and retention. Studies such as those using the Force Concepts Inventory
by Richard Hake showed that “in the traditional lecture course students master no more than 30 per cent
of the key concepts that they didn‟t already know at the start of the course.
Carl Weiman explains that research has several things to say about pedagogical strategies related to
learning. Retention of information, understanding of basic concepts, and beliefs about science and
scientific problem solving are included.
Weiman concludes that, since people learn by creating their own understanding, with assistance from
teachers, then effective teaching facilitates that creation by getting students „engaged‟ in thinking deeply
about the subject at an appropriate level and then monitoring that thinking and guiding it to be more
expert-like. These strategies include reducing cognitive load to reduce the load on the short-term
memory by slowing down, using clear explanations and connections between ideas, and using pictorial
representations as well as verbal and mathematical representations. Teachers must know where their
students are starting from in their thinking, so they can build on that foundation; they must “engage”
students and guide their thinking using technologies, and to use Novack et al‟s „just-in-time-thinking‟
through questions to be covered before class.
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Part 2: Active Involvement versus Engagement
It should be noted that the word „engaged‟ must be used with caution and there is a developing
background of theory that distinguishes „active learning‟ from „engagement‟ which has specific
implications. The Derek Bok Centre for Teaching and Learning (Harvard University) indicates that
• active learning includes in-classroom methods that involve students in the learning process requiring
meaningful learning activities that cause them to think about what they are doing.
• collaborative learning is any instructional method in which students work together in small groups
toward a common goal.
• cooperative learning is a structured form of group work when students pursue common goals whie
being assessed individually.
• problem-based learning uses relevant problems introduced at the beginning of an instructional cycle
and used to provide context and motivation for learning that follows
The definition of engagement is much more debated and is developing more specific definition with
time. Russell, Ainley and Frydenberg, 2005, (P1) define engagement as “energy in action, the
connection between person and activity”. Ball and Perry (2010) define engagement as “students‟
involvement with activities and conditions likely to generate high quality learning”
While engagement was viewed as reflecting a person‟s active involvement in a task or activity (Reeve,
Jang, Carrel, Jeon & Barch, 2004) it has now developed to include specific sub-divisions of cognitive
engagement, emotional engagement, and psychological engagement. In Finn‟s (1989) model,
engagement is comprised of behavioural (participation in class and school) and affective components
(identification, belonging, valuing learning). Similar definitions have been offered by Newmann,
Wehlage, and Lamborn (1992) and Marks (2000). More recently, engagement has been defined as
having three subtypes: behavioural (positive conduct, effort, participation), cognitive (e.g. self-
regulation, learning goals, investment in learning) and emotional or affective (e.g. interest, belonging,
and positive attitude about learning (Fredericks et al, 2004; Jimerson, Campos, & Greif, 2003).
Measuring student engagement
Kuh (2001) reports that student engagement represents the amount of time and effort that students put
into their study and learning. Pintrich and De Groot (1990) indicate that the extent to which students are
involved in active learning is thought to be facilitated by experiences that involve constructing new
knowledge and understanding. A series of five benchmarks have been established for student
engagement. These are: active and collaborative learning, academic challenge, student interactions with
faculty, enriching educational experiences, and supportive campus environment.
Bell and Perry (2010) identify the Cognitive Processes and attempt to Explain the Dominant Cognitive
Processes. They use Felder and Brent‟s argument that “students have different levels of motivation,
different attitudes about teaching and learning, and different responses to specific classroom
environments and instructional practices. The more thoroughly instructors understand the differences,
the better the chance they have of meeting the diverse learning needs of all their students.”
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Bell and Perry indicate that each cognitive type has a distinctive pattern of processes:
• for what energizes:
- Interaction with others – Extraversion
- More solitary activities – Introversion
• for what is accessed in information gathering
- Tangible, experiential awareness - Sensing
- Conceptual, symbolic awareness – Intuiting
• the process of organizing, evaluating and deciding on information
- Based on criteria or principles – Thinking
- Based on appropriateness of worth – Feeling
They indicate that each of the possible combinations of these dichotomies leads to differences in
cognitive processes.
Sharan and Goek Chin Tan describe cognitive engagement as “understanding engagement through active
or self-regulated involvement in learning”. Since this requires an understanding of the cognitive
processes involved in active learning it is important at this stage to describe Jung‟s Eight-Functions
Model.
Jung first defines the Perception Processes, recognizing the four types of perceived data (the way things
are, the way they used to be, the way they could be now, and the way they will ultimately be).
Then he defines the ways we organize experiences and make decision about the data as the Judgment
Functions
The Four Perception Processes
we use these to focus attention and gather
information
The Four Judgment Processes
We use these to organize experiences and make
decisions
Extraverted Sensing (Se) Extraverted Thinking (Te)
Introverted Sensing (Si) Introverted Thinking (Ti)
Extraverted Intuiting (Ne) Extraverted Feeling (Fe)
Introverted Intuiting (Ni) Introverted Feeling (Fi)
While all eight of these processes are available, we develop some more than others. Jung‟s theory
indicates that the dominant process is the one we most commonly use and trust. In Jung‟s theory, the
relationship between the dominant and auxiliary is the combination of how we prefer to take in
information and our preferred processing and decision making. Ball and Perry detail the Dominant and
Perception Processes and outline the possible linkages between the Eight Cognitive Processes and
Measures of Active Learning. To do this they first established if the eight cognitive processes could be
linked to the seven areas of active learning investigated by the Australian Survey of Student Engagement
(AUSSE) instrument. They needed to determine if some processes were more aligned to engagement
than others.
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Ball and Perry used a panel of three educators to determine what linkages exist between Jung‟s
Eight Functions Model and the AUSSE instrument. They determined that:
• The Introverted Intuiting (Ni) process is only highly cognitively engaged if there is the opportunity
to ask questions in-class or on-line. They have medium cognitive engagement if they are involved
in discussions and community-based projects.
• The Extroverted Intuiting (Ne) is highly cognitively engaged in many more types of activities including
working with other students both inside and outside class, making presentations, and in in-class
discussions.
Hence Ball and Perry conclude that students with a preference for a cognitive process that is Ni would
have some difficulty in achieving well in most of the active learning occasions.
Similarly, Ball and Perry determined that those students whose cognitive processes indicate a preference
for Extraverted Feeling (Fe) would be more actively involved than students with a preference for the
Introverted Feeling (Fi) processes. They tentatively concluded that it seems likely that students
preferring different dominant cognitive processes will differ in their approach to a number of the active
learning activities.
While Ball and Perry were trying to determine if students in different disciplines displayed differences in
their use of dominant cognitive processes and hence differ in their levels of engagement, in this study for
Physics 120FC at the University of Auckland, the focus will be to what extent is the choice of dominant
processes common to all students? These ideas will be developed later in the report
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Part 3 : Socio-cultural Approach and Team Based Learning
One of the major studies discussed in the 2009 report by Foster and Armstrong was the Teaching and
Learning Research Initiative (TLRI) through the New Zealand Council for Educational Research
(NZCER) :
„Understanding and Enhancing learning communities in tertiary education in science and
engineering‟
Chris Eames and Mike Forret (CSTER, University of Waikato)
The theoretical perspective was that of using a socio-cultural approach to examine a number of factors
that may impact on the tertiary science and engineering learning community. Dawes (2004),
Aufschnaiter (2003) and Welzel et al. (1999) have all discussed the nature and role of teacher-student
and student-student relationships in tertiary learning situations. They reported that students were often
influenced by only one or two significant teachers; and that teachers were conscious of te language they
used in shaping interactions. Dalgety (2002) showed that social relationships were important in tertiary
environments, with positive relationships between students influencing the choice to continue taking
science subjects. Aldridge, Fraser and Murray et al (2002) found that tertiary students‟ perceptions of
their lessons are linked to their perceptions of teaching staff.
Forret, Eames and Coll (2007) report that:
“One of the strongest themes to emerge from the case studies was the centrally important role played by relationships
in shaping the quality of teaching and learning experiences. Students and teachers in all institutions commented that
developing positive working relationships within the tertiary science and engineering community was important.
This applied to both teacher–student and student–student relationships. ...... Students and teachers saw value in students
developing relationships with each other. These relationships were seen to provide both moral and academic support in
areas such as sharing ideas and concerns about their learning, sharing notes, and collaborating in studying. Where
students were not able to develop these relationships early on in their course, they were seen to be at a disadvantage
and, therefore, both teachers and students felt it was important for opportunities to be provided for these relationships
to be developed early” (pages 17-18).
The development of positive working relationships within tertiary science was shown to be important
and applied to both teacher-student and student-student relationships. Students at all institutions
acknowledged the fundamental role that their working relationship with their teacher played in shaping
their learning experiences. Approachability of the teacher so that students can seek help; the opportunity
to form student-student learning relationships as they shared ideas, shared notes and collaborated in
study to provide both moral and academic support; making classes more enjoyable and motivating
students to learn are all important factors. While practical classes were highly valued by students, they
found it helpful to have a combination of theoretical and practical teaching sessions, particularly when
they are integrated and related in a timely way.
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The unpublished paper “ How do we keep them? …………..” (2009)
by Foster and Armstrong reports that students expressed the value of working together and the desire to
have tutorials that were recognised by more than an attendance mark. As a response to this in 2010 and
in 2011, the Physics 120 laboratory tutorials were structured to include Team Based Learning.
Team Based Learning, TBL
The TBL strategy was developed by Michaelson, Fink and Knight at the University of Oklahoma. TBL
offers a strategy that engages students in the learning process, activates them to be more responsible for
their own learning (at least partially removing the expectation of „spoon-feeding), encourages them to
attend lectures, and provides relevant, supported challenges and greater opportunity of success through
formation and development of cohesive learning teams.
The TBL Objectives:
∙ To shift the primary learning objectives of the course from simply familiarizing students
with key concepts to ensuring students learn how to use the key concepts;
∙ To change the role and function of the teachers and tutors. They will need to move from
simply delivering information, to managers of the learning and instructional processes.
∙ To change the role and function of students in the course. Instead of being recipients of
information and content, students will become responsible for initial acquisition of content
and for working collaboratively with other students.
Team Based Learning is based on four principles:
Principle 1: Groups must be properly formed and managed to minimize barriers to group
cohesiveness and to share the abilities by equitable distribution of members (avoiding previously
established relationships, or avoiding group formation based on background factors such as nationality,
culture or native language). The group members need to have a range of ability levels, physics
experience, and language fluency abilities. Learning teams should be fairly large and diverse to
maximize their intellectual diversity, yet not so large as to prevent full participation by all team
members. Optimal size is 5 to 7 students per group. Groups should be permanent so they can evolve into
effectively functioning teams.
Principle 2: Students must be made accountable:
The development of groups into cohesive learning teams requires assessing and rewarding for a number
of different kinds of behaviour. Students must:
• be accountable for preparing the work - the Readiness Assurance Process that occurs at the
beginning of each session requires students to answer 8 - 10 multiple choice questions based on pre-
assigned readings. Then after the individuals complete their answers, the group re-takes the same test.
This promotes accountability to the tutor and their team members.
• give in-class time to completing group assignments. Students have accountability for coming to
tutorial and for being ready to contribute to the achievement of the team.
• interact with each other in productive ways that leads to high quality team performance. Each team
develops a group dynamic that is more than the sum of the individual contributions each student makes.
The group dynamic for each group can be compared across teams by considering each group‟s ability to
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express their expertise about the solutions to each problem. The grading system applied in the last TBL
session includes ratings for the students‟ preparation, their contribution to the work and the quality of the
group work.
Principle 3: Team assignments must address a key learning objective of the course and promote
both learning and team developments.
It is most important that effective team assignments require full-group interaction. Assignments that
require groups to make decisions and enable them to report their decisions in a simple form will usually
encourage group interaction. Assignments must avoid the ability of groups to sub-divide the work and
limit interactions.
Principle 4: Students must receive frequent and immediate feedback.
This happens in two ways:
• Timely feed-back from Readiness Assurance Tests (RAT). Each stage of the RAT is assessed
immediately while the next stage is being implemented. The tutor has time to assess the individual multi-
choice questions while the group multi-choice test is in progress. Similarly they assess the group multi-
choice tests while the team problem solving test is in progress. The individual student and group are
supported to determine how effective their current learning procedures are by receiving this immediate
notification of their previous efforts. High scores mean they are doing what they need to be doing to
learn, and vice versa for low scores.
• Timely feedback on application focused team assignments. Students receive quick feed-back on
application-based assignments and this encourages both learning and team development.
The Way Forward
This report will now develop each of these theoretical perspectives and provide evidence of outcomes
related to each. Then it will focus back on the question
“How can we keep them?”
and attempt to respond to the challenge “Have we found the answer?”
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Part 1: Influence of Student Requests, Lecture Representative Feed-back and
Assessment Schedule Changes
In the 2009 Report “How do we keep them? Identifying and dismantling barriers to participation,
performance and progression in Stage I Physics courses‟ students requested more recognition for
tutorial involvement and achievements. Additionally, new strategies such as Team Based Learning and
pre-lecture focus-questions have been introduced and student efforts given to test revision showed the
need to discriminate the assessment rewards between assignments and tests. The perspective of Team
Based Learning shall be discussed later in this report.
Over the past five years 2006 to 2010 the Physics Department has used the on-line learning and
assessment tool known as OASIS. This is an on-line program that is used to expand student
understanding of ideas presented in lectures through active learning. It was introduced and used for
assessment purposes to prevent time being used for marking. This generates lecturer time to give student
tutorial help and to provide time to write more OASIS questions. Students are given a schedule of dates
for each of four assignments; they are given one week to practice five or six questions and one hour to
complete the assessment.
As part of the 2009 report, Foster and Armstrong asked students to comment on OASIS. There was an
even split both for and against (53% for-47% against), but students reported they simply need more time
to practice the Physics problem solving. One week practice and one hour for assessment were too
compressed in their busy schedule of learning and assessment. Students reported requiring between five
and nine hours practice on OASIS problems. Some simply found algorithms to get the correct value
rather than understanding the physics.
Therefore, as the opportunity was presented by Pearson Education, it was decided to use Mastering
Physics. While this is similar in some aspects to OASIS, the separation between skill/practice time and
assessment time was not required. Students logged-in to Mastering Physics, worked through a set of
lecturer-selected problems (both qualitative and quantitative) and entered their answers to the problems.
Their score reflected the number of times they attempted each part of each question. Part of the
assessment mark was subtracted for incorrect entry values. In addition the stress was mostly removed by
extending the time the assignment was available. Students were given twelve days to complete the
problem solving in each assignment. This allowed better planning and time-management by students.
During 2009 and 2010 the lecture representatives have provided feedback on progress and difficulties
experienced by students during the semester. Two of the most familiar complaints have been that the
pace of lectures has been too quick for many students and that there is insufficient time given to students
to become involved in the problem solving process during lectures. In an attempt to overcome the
former (pace too quick), each of the thermal lectures was given a preliminary reading and set of
questions that set the context of the lecture. These questions were followed up during lecture.
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The skill of problem solving was supported during thermal and mechanics lectures through provision of
problem-solving guidelines, student involvement in problem-solving during lectures, the use of clickers
during lectures, Mastering Physics in assignments and Team Based Learning in laboratory tutorials.
Therefore there was a coordinated, coherent emphasis on problem solving
Therefore, over the past two years, adjustments have been made to the assessment schedule. This was
needed to ensure that students would give energy and commitment to the strategy, and that the strategy
would be recognized as being valuable to the whole assessment schedule.
2009 2010 2011
Assignments 4 @ 2.5% = 10% 4 @ 2.5% = 10% 4 @ 2.5% = 10%
Tests 2 @ 5% = 10% 2 @ 5% = 10% 2 @ 6% = 12%
Team Based Learning Tutorials 7% 8%
Laboratories 15% 15% 15%
Examinations 65% 58% 55%
Total 100% 100% 100%
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Part 2: What Colour Is Student Involvement in Lectures?
The strategy for measuring „student active involvement‟ in lectures followed that by Carl Wieman
Science Education Initiative at the University of British Columbia, Vancouver, Canada. An observer
was placed in lectures at least twice per week over the final ten weeks of semester 1, 2011. They
recorded the number of students in lecture and randomly chose two groups of ten people in the lecture
theatre for each lecture. The active involvement (engagement /dis-engagement) was measured by
recording the number of people in each group who were „on-task‟ at each of the times selected. These
times were at regular ten-minute intervals.
The average attendance at lectures was 74.2% of the 308 enrolled students.
This strategy provided data from twenty lectures, giving numbers of students in each lecture,
„involvement/ dis-involvement, and recorded the strategy being used at each of the recording times.
The data and graphs are attached as Appendix 1
The sum of the number of students from both groups involved in lecture and on-task was then recorded
as the „involvement index‟ and the strategy being used at that time was recorded with the involvement
index.
There was a judgment made about each strategy. This judgment matched the extent of student
involvement to a colour, using red as student independent work (e.g. students solve problems on their
own or with partners), pink as students involved in answering clicker questions, yellow as a strategy
involving class input but led by lecturer, green as lecturer developing explanations of ideas with some
class input (structured questions), blue as explaining ideas from slides, and purple as pure content
transfer teaching.
The strategies were listed beside each „involvement index‟ and the occurrence frequency within each
„involvement index‟ recorded. The graph of „frequency versus involvement index‟ shows the relative
use of each type of strategy, and the graph of frequency trend for each „involvement index‟ value shows
the trend in use of the activity as the activity index decreases.
It would seem that while student on-task attention is relatively high, with most involvement index scores
above 12/20, most of our lecturing strategies are still „content-focused‟ strategies that provide little
opportunity for direct student involvement.
It is perhaps not surprising that when there was higher use of content-focused strategies, the involvement
index decreased significantly as shown between involvement indices 18 to 16. However these also
corresponded to use of some class-input strategies during problem solving.
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Use of Clickers and other Strategies to Promote Student Involvement
During the semester two lecturers used the Qwizdom Clickers to enhance student learning and
involvement in lectures. Students responded to the PowerPoint questions,
Data from at least seven lectures was gathered and the responses analyzed. Where fewer than 80% of
students provided the correct response this was colour coded so that the data with darkest shading
showed the most need for review.
For example
Lecture 5 Qn A/True/Yes B/False/No C D E F # %
Involved Answer %
Correct
1 8 / 6% 9 / 7% 102 / 77%
0 / 0% 0 / 0% 0 / 0% 119 38.6 C 77 %
2 20 / 15% 83 / 62% 19 / 14%
0 / 0% 0 / 0% 0 / 0% 121 39.3 B 62 %
3 29 / 22% 54 / 41% 24 / 18%
7 / 5% 0 / 0% 0 / 0% 114 37.0 B 41 %
4 11 / 8% 6 / 5% 92 / 69%
5 / 4% 0 / 0% 0 / 0% 114 37.0 C 69 %
5 92 / 69% 3 / 2% 1 / 1% 11 / 8%
0 / 0% 0 / 0% 107 34.7 A 69 %
From these a document „Comments from Clicker Questions‟ was placed on CECIL. This gave feedback
about the questions that showed the lowest correct response rate.
In a typical lecture only 30 to 40% of students in the lecture were using Clickers.
End of Course Survey
The end-of-course survey provided students with questions related to the use of clickers,
problem-solving and Power-Point in lectures. Students used a Likert scale with 1 (strongly disagree) and
10 (strongly agree). The questions asked to what extent the strategy supported their learning.
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In the „End of Course Survey‟ the following questions and responses were given:
Question:
Lecturers‟ use of Clickers in lectures and the
correct explanations as part of that significantly
helped me to improve my understanding.
Data:
Mean = 4.91
Standard Deviation-A (σn-1) = 2.75
While there was a large group that did not respond
well to the use of clickers, there was a reasonably
even spread of student responses, with over 54%
(137) of students expressing support for their use.
It would seem that there is greater potential for use
of clickers in lectures by requiring use of clickers
as part of the assessment system, even if the
percentage recognition is low. This would at least
give lecturers a better view of student learning and
increase active involvement of students.
Question
Lecturers‟ use of problem solving in lectures
significantly helped me to improve my
understanding.
Data:
Mean = 6.61
Standard Deviation-A (σn-1) = 2.35
There was overwhelming support by students for
use of problem solving in lectures so that students
could have time to think about strategies to solve
the question. Over 80 % of students showed a
positive response to this strategy.
Question
Lecturers‟ use of Power-Point explanations in
lectures significantly helped me to improve my
understanding.
Data:
Mean = 6.22
Standard Deviation-A (σn-1) = 2.26
Over 78% of students supported the use of
PowerPoint presentations to significantly help them
improve their understanding.
It is suggested that this needs to be communicated
to lecturers who still do not use this strategy; there
may be a need for some professional development.
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Lecturers' use of PowerPoint
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Question:
Lecturers‟ use of write-on-board explanations in
lectures significantly helped me to improve my
understanding.
Data:
Mean = 6.40
Standard Deviation-A (σn-1) = 2.43
Not surprisingly, over 80% of students supported
the use of write-on-board explanations. However,
other lectures use equally effective strategies such
as fill-in notes and these give students greater
opportunity for involvement.
The purposeful use of problem solving, clicker strategies, involvement of students in opportunities of
perception checks and development of explanations using think-pair-share strategy, does provide
reasonable incentive for students to appreciate the use of these strategies and to stay on-task. This is
supported by the attendance-at-lectures data shown in Appendix 2 as percentage attendance.
It seems that continued development of in-lecture strategies to involve students more directly is required
if students are to understand and remember ideas more effectively and if they are not to experience
cognitive overload. During this semester at least one lecturer encouraged students to use pre-lecture
questions and reading of the text and Power Point slides to support effective in-lecture learning. If
lectures can be developed as „lect-orials‟ then student learning may be enhanced and achievement
improved.
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Lecturers' use of write-on-board explanations
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Part 3: Involvement and Cognitive Engagement
Students were asked to respond to several questions that were intended to help lecturers determine if the
balance of support strategies and activities helped students to achieve better learning and assessment
scores. It was the same survey used in 2010 with approval from the University of Auckland Human
Participants Ethics Committee Ref 2010/228.
Students scored their responses on a Likert scale with 1 being low and 10 being high. Some questions
were deliberately given as the negative sense. There were 308 students enrolled and 252 responses were
received from students. This gave a Confidence Level of 99% with a confidence interval of 2.03.
While the survey covered a wider range of aspects only aspects of engagement will be reported here.
The questions, survey data and comments are provided sequentially below:
Question:
I felt quite isolated as a student with little support
available as I studied Physics 120
Data:
Mean = 3.31
Standard Deviation-A (σn-1) = 1.90
More than 68 % of students (± 1 σ) indicated they
were not isolated, rather they felt supported in the
study of Physics 120FC.
Question:
I worked most often on my own and not with other
Physics 120 students except when I needed to in
Laboratory Tutorials
Data:
Mean = 5.25
Standard Deviation-A ((σn-1) = 2.61
That this average score is not higher is good. It
indicates that students are working reasonably
cooperatively even in aspects of the course excluding
laboratory tutorials where cooperation was essential.
At the start of the semester students were encouraged
to form work groups and share their learning. This
occurred since research of strategies such as Team
Based Learning has shown the very positive effects
on achievement that occur when cooperative learning
occurs. This response seems to indicate that students
are benefiting from this encouragement.
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30
40
50
60
70
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1. Isolated or not?
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I worked on my own unless required to work in a group
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The next several questions sought to distinguish between „active involvement‟ and „engagement‟ by
asking questions that relate the person to activity, as indicated by Russell, Ainley and Frydenberg, 2005,
(P1). These questions may not provide the ability to discern if the named activity type provided “ the
conditions to generate high quality learning” as in the definition by Ball and Perry (2010). However in
this report the intended focus is on cognitive engagement.
The questions were developed to utilize Jung‟s Eight-Functions Model in which he defines the
Perception Processes and the Judgment Functions and then indicates that the dominant process is the
one we most commonly use and trust. In Jung‟s theory, the relationship between the dominant and
auxiliary is the combination of how we prefer to take in information and our preferred processing and
decision making.
The Four Perception Processes
we use these to focus attention and gather
information
The Four Judgment Processes
We use these to organize experiences and make
decisions
Extraverted Sensing (Se) Extraverted Thinking (Te)
Introverted Sensing (Si) Introverted Thinking (Ti)
Extraverted Intuiting (Ne) Extraverted Feeling (Fe)
Introverted Intuiting (Ni) Introverted Feeling (Fi)
Question:
I preferred learning activities that provided hand-on
experiences (interactive & experiments) and did not
prefer the conceptual & symbolic (book-type)
learning.
Data:
Mean = 5.96
Standard Deviation-A (σn-1) = 2.49
The data seems to indicate we have a balance of
students that have a slight bias toward extraverted
intuiting (Ne). When Ball and Perry‟s theory is
applied to the Physics 120 cohort it is possible to
identify that the students in Physics 120 do have a
bias towards being highly cognitively and being able
to be engaged in many more types of activities
including working with other students both inside
and outside class, making presentations, and in in-
class discussions.
It seems we should be reasonably confident that we
can increase the range of activities including class
discussions and other interactions and know that
most students will be capable of good cognitive
engagement in these.
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I preferred hands-on experiences
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Question:
I preferred learning activities in Physics 120 that
provided learning about ideas in Physics that had
applications and worth and did not prefer learning
through thinking and deciding type activities.
Data:
Mean = 6.19
Standard Deviation-A (σn-1) = 2.18
The student cohort is strongly Extroverted Feeling
(Fe) in Jung‟s Judgment Processes. This indicates
that over 68% (± 0.5 Standard Deviation-A) do
prefer to learn ideas that have application and worth.
It signals to lecturers that leaning may occur best if
contextual learning is used, giving actual
applications.
Question:
Physics 120 has provided experiences that have
required me to respond by using most of my senses
including touch, sight, hearing etc.
Data:
Mean = 4.93
Standard Deviation-A (σn-1) = 2.26
This question tested the extent to which students
used the Perception process of extroverted sensing.
There is a reasonable balance showing that only
about half of the students in the cohort utilized their
senses. It signals that we might need to use a greater
variety of strategies to encourage students to
perceive ideas fully
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Preferred learning activities that showed applications
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I have needed to respond by using most of my senses
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Question:
Physics 120 has required me to gather and organize a
large amount of information and I have been able to
compare ideas to earlier learning about Physics. This
has allowed me to develop a more accurate
understanding about Physics.
Data:
Mean = 6.35
Standard Deviation-A (σn-1) = 2.07
This question was intended to determine the degree
of introverted sensing used by students. There is a
strong bias of students with over 70% expressing that
they were able to gather and organize, compare
earlier learning and to gain a more accurate idea
about Physics through the teaching and learning
strategies used.
Question
Physics 120 has enabled me to see some new and
different possibilities in Physics ideas and to better
understand relationships between ideas and
applications.
Data:
Mean = 6.56
Standard Deviation-A (σn-1) = 2.11
This question was intended to determine the relative
bias on the Extroverted Intuiting (Ne) Perception
Process. Ball and Perry indicate that the Extroverted
Intuiting (Ne) student is highly cognitively engaged
in many more types of activities including working
with other students both inside and outside class,
and in in-class discussions.
It would seem that, since over 78% of Physics 120
students indicated a score greater than 5.40, the
cohort of students is highly engaged in cognitive
activities.
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I have needed to gather, organise and compare earlier learning
Series1
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Physics 120 has allowed new ideas and understand relationships better
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Question
Physics 120 has allowed me to see ideas from
several perspectives and allowed me to better
understand ideas by correcting ideas I did not
understand correctly. It has required different ways
of thinking about ideas.
Data:
Mean = 6.37
Standard Deviation-A (σn-1) = 1.95
This question was intended to determine the relative
bias on the Introverted Intuiting Perception Process.
Again the data shows that over 70% of students are
also strongly able to personally consider and
perceive ideas from several perspectives and have
shown divergent thinking processes, enabling them
to correct misconceptions.
By applying Jung‟s „Eight-Functions Model‟ it has been possible to show that, in terms of Cognitive
Engagement, the 2011 cohort of Physics 120 FC students are strong in all of the Perception Processes,
though there is a need for more strategies that will support the use of more student senses to develop
their extroverted sensing Perception Processes. This identifies a potential area for teaching strategy
development.
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Physics 120 has shown different perspectives and understand ideas better
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Part 4: Applying socio-cultural theory using modified Team Based Learning
Significant changes were made to the Team Based Learning (TBL) strategy. In 2010 an Ethics
Committee approved survey was used to make the division of students into their random groups. The
criteria used enabled equivalent groupings using criteria of Physics experience, attitude to Physics and
Mathematics and gender/ethnic mixture. When TBL was introduced it was necessary for students to
adapt to both the strategy and the people in their group. So that this could occur with the additional stress
of assessment, the first session was not assessed and only the last three sessions were assessed.
In 2011 the four TBL tutorials took place after all five experiments were completed and the TBL groups
were identical to the groupings for their experimental sessions. This meant that students were already
familiar with their peers and were able to quickly adapt to the new TBL strategy without having to adapt
to new people in their group. All four TBL sessions were assessed.
The responses to the intervention of using Team-Based Learning were measured by looking at both
individual and team scores as the team strategy proceeded. and through the student survey that sought a
Likert scale response to questions. The first question was categorised as „student responses to the TBL
strategy‟, while the other two questions gave a measure of „immersion-into-Physics responses resulting
from the inclusion of more engagement strategies in physics 120‟. The former provides evidence of
differences in achievement made by using Team based Learning as an intervention strategy to improve
learning, while the latter provides evidence of the students‟ perceptions about their engagement in and
commitment to the course.
Question:
The Team Based Learning strategy in lab tutorial
time was effective in developing group work and
shared learning techniques.
Data:
Mean = 6.80
Standard Deviation-A (σn-1) = 2.43
Students expressed very strong support for TBL.
Informal feedback after TBL sessions provided
expressions of enjoyment and genuine involvement
of teams. Over 77% expressed support for this
strategy. The graph of response distribution shows
the high support by the majority of students through
its heavy bias at scores ≥ 5.
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The Team Based Learning effectively helped develop group work and shared
learning
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Question:
The Team Based Learning strategy did help me
improve my learning about the main Physics ideas.
Data:
Mean = 6.45
Standard Deviation-A (σn-1) = 2.37
Students expressed strongly that Team Based
Learning did help them improve their learning.
Again over 77% expressed support through
scores ≥ 5.
Question:
The Team Based Learning strategy did help me to
improve my score in assessment more than I could
have done through personal effort.
Data:
Mean = 5.93
Standard Deviation-A (σn-1) = 2.45
Over 73 % of students recorded scores ≥ 5 showing
very strong support for TBL as a strategy that helps
improve their assessment scores.
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Team Based Learning did help me improve my learning about the main Physics ideas
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Team Based Learning did help me to improve my assessment scores
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To test the effectiveness of Team Based Learning the individual scores of all students in the multiple
choice were averaged and compared to the average scores of the teams‟ multiple choice. The average
scores for the team-based problem solving are also provided.
Average
score for
individuals‟
multiple
choice /15
Standard
deviation-A
(σn-1)
Average
score for
team-based
multiple
choice /15
Standard
deviation-A
(σn-1)
Average
score for
team-based
problem
solving /15
Standard
deviation-A
(σn-1)
2010 7.24 3.63 10.41 4.43 9.88 4.62
2011 7.10 1.85 8.55 5.10 9.47 5.54
The average scores for the team-based multiple choice are nearly one standard deviation above the
average scores for the individual multiple choice, and the average scores for the team-based problem
solving are approximately 0.5 standard deviation-A above the individual multiple choice. This shows a
significant improvement in learning over individual effort.
After the Team-Based Learning sessions each group member was provided with a feed-back sheet.
Some of the comments were:
“ Good input from each member. Idea well explained so each person understood”
“My group members worked well by sharing their ideas and knowledge on questions.”
“E,A and E contributed a lot towards problem solving. E and A pointed out a lot of important points and
formulas regarding the problems. Overall I think we were an effective team.”
“Team-based learning is kind of fun. Being able to work in a group and all.”
It seems that team-based learning was well received and contributed effectively to improving student
learning and understanding.
Is this the Answer?
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Part 5: First-half semester survey 2011
A survey at the end of the first half of the semester was used to determine student responses to strategies
being used at that time. After providing a whole class survey, 150 of their surveys were randomly
selected and analyzed. The students rated the answer using a 1 to 5 Likert Scale. After calculations of
averages and Standard-Deviation was completed the data was considered
Average + StDevA < 2.50 approx. was ranked 4
Average + StDevA < 3.0 approx. was ranked 3
Average + StDevA < 3.2 approx. was ranked 2
Average + StDevA < 3.4 was ranked 1
Thermal and Thermodynamics Average StDevA Rank
I really enjoyed the interactive teaching techniques during Thermal
lectures 2.56 0.86 1
I really enjoyed the clicker questions 2.11 0.90 3
I found the explanations given to clicker Qs to be helpful to my learning 2.29 0.85 2
I really enjoyed then discussion and perception check opportunities 2.60 0.84 1
I enjoyed seeing the demonstrations and practicals in lectures 1.83 0.89 3
I found the pre-quiz questions to be very helpful to my learning 2.52 0.86 1
I found then pre-reading to be helpful to my learning 2.26 0.87 2
What two things contributed most to my learning during thermal
lectures? #
Pre -reading 44
Pre-lecture quiz questions 9
Lecture objectives 16
Clicker questions 34
In Class problem solving and discussions 79
Explanations of ideas 75
Mechanics Average StDevA Rank
I really enjoyed the interactive teaching techniques during mechanics
lectures 1.40 0.59 4
I really enjoyed the clicker questions 2.20 0.90 2.5
I found the explanations given to clicker Qs to be helpful to my learning 2.11 0.83 3
I really enjoyed then discussion and perception check opportunities 1.79 0.73 4
I enjoyed seeing the demonstrations and practicals in lectures 1.23 0.47 4
What two things contributed most to my learning during mechanics
lectures #
Pre-reading 22
Clicker questions 17
In-class problem solving 96
Explanations of ideas 96
This survey has a 95% confidence level and
a confidence interval of 5.67.
N (c) = 300 and n (s) = 150
Is this the Answer?
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If only Rank 3 & 4 statements are considered:
> Students enjoyed the interactive teaching techniques in Mechanics.
> Students really enjoyed then discussion and perception check opportunities in Mechanics
> students greatly enjoyed seeing demonstrations and practicals in both Mechanics and Thermal
> Students enjoyed using the Clicker questions in both Mechanics and Thermal & the explanations to
Clicker Qs in mechanics.
Rank 2 statements showed explanations to clicker Qs were enjoyed in thermal
Is this the Answer?
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Summative Effectiveness of the Strategies – Internal Assessments and Examination
To determine the effectiveness of the introduced and modified strategies in 2011 it is necessary to
consider the components of the internal assessments
2009 Exam Lab Assign T1 T2
n 299 316 306 306 291
Average 50.43 12.04 6.79 2.68 2.51
St Dev A 20.58 2.19 2.63 0.90 0.82
Total value 100.00 15.00 10.00 5.00 5.00
2010 Exam Lab Assign T1 T2 Lab Tut Total
n 277 299
294 289 274 288
Average 43.47 10.85 5.39 2.25 2.40 4.14
St Dev A 20.37 2.47 2.86 1.01 0.89 1.36
Total Value 100.00 15.00 10.00 5.00 5.00 7.00 100.0
2011 Exam Lab Assign T1 T2 Lab Tut Lab Tut Total
n 277 307 292 295 278 303 303
Average 27.96 11.51 7.72 2.40 2.82 4.44 5.14
St Dev A 11.97 2.35 2.43 0.97 1.02 1.62 1.77
Total Value 55.00 15.00 10.00 *5.00 *5.00 7.00 8.00 100.0
* These assessed values were scaled from total / 6 to total /5
Is this the Answer?
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Data Analysis: T Tests
This is used to assess whether the averages of two groups are statistically different from each other. It is
particularly appropriate as the analysis for the post-test-only two-group randomized experimental design.
The following data compares 2011 achievements with each of 2009 and 2010.
Test 1 and Test 2 Test 1 Test 2
2011 2010 2009 2011 2010 2009
Total score 5.0 5.0 5.0 5.0 5.0 5.0
n 295 289 306 278 274 291
average 2.40 2.25 2.68 2.82 2.40 2.51
Std-devA 0.97 1.01 0.90 1.02 0.89 0.82
Variance 0.941 1.02 0.81 1.04 0.79 0.67
variance / n 3.189E-3 3.529E-3 2.647E-3 3.742E-3 2.883E-3 2.302E-3
Test 1
T–test (2011-2009) -3.66 Degrees freedom 599 Probability -1.96
T-test (2011 – 2010) +1.83 582 1.96
Risk Level , α = 0.05 (95% confidence)
Since the T-value for 2011-2009 is larger than the probability values, the Test 1 achievement of the 2009
cohort was higher than that of the 2011 cohort.
The T-value for 2011-2010 Test 1 is not larger than the probability so there is no significant difference
between these two tests.
Test 2
T–test (2011-2009) +3.98 567 +1.65
T-test (2011 – 2010) + 5.16 550 +1.65
Risk Level , α = 0.05 (95% confidence)
Since the T-values for both the 2011-2009 and the 2011-2010 cohorts are higher than the probability
values, the Test 2 achievement of the 2011 cohort was higher than either the 2010 or 2009 cohorts.
While there was no significant statistical difference show by Test 1, there were significant differences
between the 2009-2011 Test 2 and 2010-2011 Test 2 scores. This may be the result of the accumulative
effort being made throughout the semester, or from a difference in test standard.
Is this the Answer?
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Assignments
Assignments Total
2011 2010 2009
Total score 10.0 10.0 10.0
n 292 294 306
average 7.72 5.39 6.79
Std-devA 2.43 2.86 2.63
Variance 5.905 8.180 6.917
variance / n 0.0202 0.0278 0.0226
Assignments
T–test (2011-2009) 4.49 Degrees freedom 596 Probability 1.96
T-test (2011 – 2010) 10.63 584 1.96
Risk Level , α = 0.05 (95% confidence)
Since the T-values for both the 2011-2009 and the 2011-2010 cohorts are higher than the probability
values, the total assignment achievement of the 2011 cohort was higher than either the 2010 or 2009
cohorts. It seems that some combination of the strategies implemented is contributing to increase total
test scores.
Laboratory Tutorials
Assignments Total
2011 2010
Total score 7.0* 7.0
n 303 288
average 4.44 4.14
Std-devA 1.62 1.36
Variance 2.624 1.850
variance / n 0.00866 0.006423
Lab Tutorials
T–test (2011-2010) 2.44 Degrees freedom 589 Probability 1.96
Risk Level , α = 0.05 (95% confidence)
Since the T-value for 2011-2010 is larger than the probability, the achievement of the 2011 cohort was
significantly higher than that of the 2010 cohort. The students combined efforts in laboratory tutorials is
increasing their scores significantly. The removal of the peer assessment was likely to be a significant
factor contributing to this increase in the laboratory tutorial scores.
Is this the Answer?
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Total Internal and External Achievements Comparison
To determine if total internal assessments contributed to the improved achievement, or if the exam was the major
source of improvement, the internal assessments were compared , then the raw exam assessments compard using
the T-test.
Internal Assessments
Total Internal
2011 2010 2009
Total score 100* 100* 100*
n 303 306 316
average 66.15 57.06 67.24
Std-devA 17.35 18.92 16.17
Variance 301.0 357.9 261.5
variance / n 0.9934 1.1696 0.8275
Total Internals
T–test (2011-2010) 6.18 Degrees freedom 607 Probability 1.96
T-test (2011-2009) -0.807 617 1.96
Risk Level , α = 0.05 (95% confidence)
Since the T-value for 2011-2010 is larger than the probability, the internal assessment achievement of
the 2011 cohort was significantly higher than that of the 2010 cohort.
Since the T-value for 2011-2009 is slightly smaller than the probability but less than the probability
value, the internal assessments achievement of the 2011 cohort was not significantly different than that
of the 2010 cohort. There was a significant increase in the internal achievement scores between 2010 and
2011.
Is this the Answer?
33
Examination Assessment
Examination
2011 2010 2009
Total score 100* 100* 100*
n 276 277 299
average 51.03 43.37 50.43
Std-devA 21.58 20.37 20.58
Variance 465.69 414.93 423.53
variance / n 1.6873 1.4979 1.4165
Examination
T–test (2011-2010) 4.29 Degrees freedom 551 Probability 1.96
T-test (2011-2009) 0.005 573 1.96
Risk Level , α = 0.05 (95% confidence)
Since the T-value for the examination assessment 2011-2010 is larger than the probability, the
achievement of the 2011 cohort was significantly higher than that of the 2010 cohort.
Since the T-value for the examination assessment 2011-2009 is smaller than the probability, the
achievement of the 2011 cohort was not significantly different than that of the 2009 cohort.
There was a significant increase in the examination assessment scores between 2010 and 2011.
Is this the Answer?
34
Conclusions and Recommendations
1. What Colour Is Student Involvement in Lectures?
The average attendance at lectures was 74.2% of the 308 enrolled students.
It would seem that while student on-task attention is relatively high, with most involvement index
scores above 12/20, most of our lecturing strategies are still „content-focused‟ strategies that provide
little opportunity for direct student involvement.
It is perhaps not surprising that when there was higher use of content-focused strategies, the
involvement index decreased significantly as shown between involvement indices 18 to 16. However
these also corresponded to use of some class-input strategies during problem solving.
2. Use of Clicker Response system
From these a document „Comments from Clicker Questions‟ was placed on CECIL. This gave
feedback about the questions that showed the lowest correct response rate.
In a typical lecture only 30 to 40% of students in the lecture were using Clickers.
While there was a large group that did not respond well to the use of clickers, there was a reasonably
even spread of student responses, with over 54% (137) of students expressing support for their use. It
would seem that there is greater potential for use of clickers in lectures by requiring use of clickers as
part of the assessment system, even if the percentage recognition is low. This would at least give
lecturers a better view of student learning and increase active involvement of students.
Recommendation: That the use of clickers is extended to all lecturers in this course and that
there be an assessment component for participation with the clickers.
3. There was overwhelming support by students for use of problem solving in lectures so that students
could have time to think about strategies to solve the question. Over 80 % of students showed a
positive response to this strategy.
4. Over 78% of students supported the use of PowerPoint presentations to significantly help them
improve their understanding. It is suggested that this needs to be communicated to lecturers who still
do not use this strategy; there may be a need for some professional development.
5. Not surprisingly, over 80% of students supported the use of write-on-board explanations. However,
other lectures use equally effective strategies such as fill-in notes and these give students greater
opportunity for involvement.
Recommendation: The Physics Department should provide development of in-lecture strategies
to involve students more directly is required if students are to understand and
remember ideas more effectively and if they are not to experience cognitive overload.
Is this the Answer?
35
Recommendation: All lecturers should develop use of pre-lecture questions and reading of the text
and Power Point slides to support effective in-lecture learnin so that lectures
can evolve towards being more in the „lect-orial‟ style.
6. Since more than 68 % of students (± 1 σ) indicated they felt supported in the study of Physics 120FC
the combination of strategies should be continued.
7. The combination of strategies indicates that students are working reasonably cooperatively even in
aspects of the course excluding laboratory tutorials where cooperation was essential. Strategies such
as Team Based Learning show very positive effects on achievement that occur when cooperative
learning occurs. This response seems to indicate that students are benefiting from this
encouragement.
Cognitive Engagement
8. The data seems to indicate we have a balance of students that have a slight bias toward extraverted
intuiting (Ne). Ball and Perry‟s theory identifies that the students in Physics 120 do have a bias
towards being highly cognitively and being able to be engaged in many more types of activities
including working with other students both inside and outside class, making presentations, and in in-
class discussions.
It seems we should be reasonably confident that we can increase the range of activities including class
discussions and other interactions and know that most students will be capable of good cognitive
engagement in these.
9. The student cohort is strongly Extroverted Feeling (Fe) in Jung‟s Judgment Processes. This indicates
that over 68% (± 0.5 Standard Deviation-A) do prefer to learn ideas that have application and worth.
It signals to lecturers that leaning may occur best if contextual learning is used, giving actual
applications.
10. This question tested the extent to which students used the Perception process of extroverted
sensing. There is a reasonable balance showing that only about half of the students in the cohort
utilized their senses.
Recommendation: As part of lecture preparation there needs to be awareness by the lecturer to use a
greater variety of strategies to encourage students to perceive ideas fully
11. There is a strong bias of students showing introverted sensing. Over 70% expressed that they were
able to gather and organize, compare earlier learning and to gain a more accurate idea about Physics
through the teaching and learning strategies used.
Is this the Answer?
36
12. Ball and Perry indicate that the Extroverted Intuiting (Ne) student is highly cognitively engaged
in many more types of activities including working with other students both inside and outside class,
and in in-class discussions. It would seem that, since over 78% of Physics 120 students indicated a
score greater than 5.40, the cohort of students was highly engaged in cognitive activities.
13. In the question intended to determine the relative bias on the Introverted Intuiting Perception
Process, the data shows that over 70% of students are also strongly able to personally consider and
perceive ideas from several perspectives and have shown divergent thinking processes, enabling them
to correct misconceptions.
14. By applying Jung‟s „Eight-Functions Model‟ it has been possible to show that, in terms of
Cognitive Engagement, the 2011 cohort of Physics 120 FC students are strong in all of the Perception
Processes though there is a need for more strategies that will support the use of more student senses to
develop their extroverted sensing Perception Processes. This identifies a potential area for teaching
strategy development.
Applying socio-cultural theory using modified Team Based Learning
15. Students expressed very strong support for TBL. Informal feedback after TBL sessions provided
expressions of enjoyment and genuine involvement of teams. Over 77% expressed support for this
strategy. The graph of response distribution shows the high support by the majority of students
through its heavy bias at scores ≥ 5.
16. Students expressed strongly that Team Based Learning did help them improve their learning.
Again over 77% expressed support through scores ≥ 5.
17. Over 73 % of students recorded scores ≥ 5 showing very strong support for TBL as a strategy
that helps improve their assessment scores.
18. It seems that team-based learning was well received and contributed effectively to improving
student learning and understanding.
Quantitative analysis using T-Tests
19. The 2011 cohort‟s on-course achievements have shown to be significantly higher than the
achievements of either the 2009 or 2010 cohorts‟ achievements. The assignment and tutorial
strategies used have both contributed to the summative improvement in learning and achievement.
The tests have not contributed to any improvement to the on-course achievements though the Test 2
scores of the 2011 cohort have improved significantly as a result of the strategies being applied.
Is this the Answer?
37
20. The histograms attached as Appendix 3 show a gradual trend toward the decrease in the number
of students achieving in the D and D+ grade groups and a significant shift of these to the C and C-
groups.
21. While there was no significant statistical difference shown by Test 1, there were significant
differences between the 2009-2011 Test 2 and 2010-2011 Test 2 scores. This may be the result of the
accumulative effort being made throughout the semester, or from a difference in test standard.
22. Since the T-values for both the 2011-2009 and the 2011-2010 cohorts are higher than the
probability values, the total assignment achievement of the 2011 cohort was higher than either the
2010 or 2009 cohorts. It seems that some combination of the strategies implemented is contributing to
increase total test scores.
23. Since the T-value for 2011-2010 is larger than the probability, the achievement of the 2011
cohort laboratory tutorials was significantly higher than that of the 2010 cohort. The students
combined efforts in laboratory tutorials is increasing their scores significantly. The removal of the
peer assessment was likely to be a significant factor contributing to this increase in the laboratory
tutorial scores.
Recommendation:
Do not re-introduce the peer assessment strategy; rather consider use of bonus marks for the best
performing team each session.
24. Since the T-value for 2011-2009 is slightly smaller than the probability but less than the
probability value, the internal assessments achievement of the 2011 cohort was not significantly
different than that of the 2010 cohort. There was a significant increase in the internal achievement
scores between 2010 and 2011.
25. Since the T-value for the examination assessment 2011-2010 is larger than the probability, the
examination achievement of the 2011 cohort was significantly higher than that of the 2010 cohort.
Since the T-value for the examination assessment 2011-2009 is smaller than the probability, the
achievement of the 2011 cohort was not significantly different than that of the 2009 cohort. There was
a significant increase in the examination assessment scores between 2010 and 2011.
It is not possible to identify that the laboratory tutorials (Team Based learning) contributed significantly
to the improved achievement of students since there was a significant change made to the strategy.
Similarly it would be unwise to indicate that any other individual strategy contributed most significantly
to the improved achievement of students
Is this the Answer?
38
However, it seems that the combined effects of the changes resulting in the much greater
„student-centred‟ approach have significantly improved student achievement.
It was never intended to demonstrate and identify the relative effectiveness of one strategy over another
in this report. Rather, it has been possible to show that it has been possible to develop a combination of
strategies that together contribute to more effective learning and achievement. These strategies have
come from reciprocal development of stronger relationships between the course coordinator, lecturers,
tutors, demonstrators and students.
„The answer‟ seems to be that the strategies used should develop relationships, recognize and develop
student cognitive engagement, and provide a variety of learning and assessment approaches that builds
student efficacy and effectiveness. We may not know whether we can „keep them‟ immediately, but the
combination of strategies should contribute to students being encouraged to stay in Physics as they build
confidence and performance that competes with their performance in other subject departments.
“The idea itself -- getting students to solve problems in groups to make them both interact and learn -- is
great. Personally, I've witnessed a case when a group of 4 guys literally rose from not caring and doing
poorly to perfect work, understanding and enjoying the physics. That was highly satisfying to see, and I
think they loved that they could do it, too.”
Sophie Shamailov, graduate Physics student and Laboratory Tutor
The quality of your life is the quality of your relationships.
Anthony Robbins
Course Coordinator
Lecturers
Tutors
STUDENTS
Lab Demonstrators
Is this the Answer?
39
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Is this the Answer?
42
Appendix 1:
What Colour is Physics Student Active Involvement?
An observer in lectures randomly selected two groups of students in each lecture and recorded the
number of students who seemed to be on-task every 5 minutes. The lecture activity was also recorded .
Colours were assigned to the type of activity: red being activities that required independent work by the
student, orange being activities that were mostly student involvement, yellow
Data is presented in both tabular and graphical formats. Dated data displays for the first half of
Semester 1 2011 are provided.
Is this the Answer?
43
1st March
Time Group 1 Group 2
9:10 a.m. 10 10
9:15 a.m. 9 9
9:20 a.m. 10 8
9:25 a.m. 10 10
9:30 a.m. 9 10
9:35 a.m. 8 10
9:40 a.m. 8 9
9:45 a.m. 9 9
9:50 a.m. 8 8
9:55 a.m. 6 7
3rd March
Time Group 1 Group 2
9:10 a.m. 10 10
9:15 a.m. 10 10
9:20 a.m. 10 10
9:25 a.m. 8 9
9:30 a.m. 10 10
9:35 a.m. 9 9
9:40 a.m. 9 9
9:45 a.m. 9 9
9:50 a.m. 8 9
9:55 a.m. 8 10
10th March Vectors
Time Group 1 Group 2
9:10 a.m. 9 10
9:15 a.m. 8 10
9:20 a.m. 10 10
9:25 a.m. 8 10
9:30 a.m. 8 9
9:35 a.m. 10 10
9:40 a.m. 10 10
9:45 a.m. 10 10
9:50 a.m. 10 10
0
2
4
6
8
10
12
9:10 AM
9:15 AM
9:20 AM
9:25 AM
9:30 AM
9:35 AM
9:40 AM
9:45 AM
9:50 AM
9:55 AM
# o
f st
ud
en
ts e
nga
ged
in le
ctu
re 1 03 11
Series1
Series2
0
2
4
6
8
10
12
9:10 AM
9:15 AM
9:20 AM
9:25 AM
9:30 AM
9:35 AM
9:40 AM
9:45 AM
9:50 AM
9:55 AM
# o
f st
ud
en
ts e
nga
ged
in le
ctu
re 3 03 11
Series1
Series2
0
2
4
6
8
10
12
9:10 AM
9:15 AM
9:20 AM
9:25 AM
9:30 AM
9:35 AM
9:40 AM
9:45 AM
9:50 AM
# o
f st
ud
en
ts e
nga
ged
in
lect
ure
10 03 11
Series1
Series2
Is this the Answer?
44
7th March Gas Laws
Time Group 1 Group 2
9:10 a.m. 10 10
9:15 a.m. 9 9
9:20 a.m. 9 10
9:25 a.m. 10 10
9:30 a.m. 10 10
9:35 a.m. 7 8
9:40 a.m. 7 7
9:45 a.m. 9 8
9:50 a.m. 9 8
9:55 a.m. 9 9
15th March Thermal transfers
Time Group 1 Group 2
9:10 a.m. 10 8
9:15 a.m. 8 10
9:20 a.m. 8 9
9:25 a.m. 6 9
9:30 a.m. 6 7
9:35 a.m. 8 9
9:40 a.m. 6 9
9:45 a.m. 10 10
9:50 a.m. 10 10
21st March Thermodynamic Processes
Time Group 1 Group 2
9:10 a.m. 10 10
9:15 a.m. 10 10
9:20 a.m. 9 8
9:25 a.m. 10 7
9:30 a.m. 10 10
9:35 a.m. 10 8
9:40 a.m. 7 7
9:45 a.m. 10 9
9:50 a.m. 8 7
0
2
4
6
8
10
12
9:10 AM
9:15 AM
9:20 AM
9:25 AM
9:30 AM
9:35 AM
9:40 AM
9:45 AM
9:50 AM
9:55 AM
# o
f st
ud
en
ts e
nga
ged
in le
ctu
re 7 03 11
Series1
Series2
0
2
4
6
8
10
12
9:10 AM
9:15 AM
9:20 AM
9:25 AM
9:30 AM
9:35 AM
9:40 AM
9:45 AM
9:50 AM
# o
f st
ud
en
ts e
nga
ged
in le
ctu
re 15 03 11
Series1
Series2
0
2
4
6
8
10
12
9:10 AM
9:15 AM
9:20 AM
9:25 AM
9:30 AM
9:35 AM
9:40 AM
9:45 AM
9:50 AM
# o
f st
ud
en
ts e
nga
ged
in le
ctu
re
21 03 11
Series1
Series2
Is this the Answer?
45
17th March
Time Group 1 Group 2
9:10 a.m. 9 9
9:15 a.m. 9 10
9:20 a.m. 10 8
9:25 a.m. 7 10
9:30 a.m. 8 7
9:35 a.m. 10 10
9:40 a.m. 10 10
9:45 a.m. 10 9
9:50 a.m. 9 10
24th March
Time Group 1 Group 2
9:10 a.m. 9 10
9:15 a.m. 10 10
9:20 a.m. 10 10
9:25 a.m. 7 10
9:30 a.m. 8 10
9:35 a.m. 8 7
9:40 a.m. 8 10
9:45 a.m. 8 10
9:50 a.m. 8 7
31st March
Time Group 1 Group 2
9:10 a.m. 8 10
9:15 a.m. 7 10
9:20 a.m. 9 9
9:25 a.m. 9 9
9:30 a.m. 6 8
9:35 a.m. 10 10
9:40 a.m. 8 8
9:45 a.m. 6 8
9:50 a.m. 7 10
0
2
4
6
8
10
12
9:10 AM
9:15 AM
9:20 AM
9:25 AM
9:30 AM
9:35 AM
9:40 AM
9:45 AM
9:50 AM
# o
f st
ud
en
ts e
nga
ged
in
lect
ure
17 03 11
Series1
Series2
0
2
4
6
8
10
12
9:10 AM
9:15 AM
9:20 AM
9:25 AM
9:30 AM
9:35 AM
9:40 AM
9:45 AM
9:50 AM
# o
f st
ud
en
ts e
nga
ged
in
lect
ure
24 03 11
Series1
Series2
0
2
4
6
8
10
12
9:10 AM
9:15 AM
9:20 AM
9:25 AM
9:30 AM
9:35 AM
9:40 AM
9:45 AM
9:50 AM
# o
f st
ud
en
ts e
nga
ged
in
lect
ure
24 03 11
Series1
Series2
Is this the Answer?
46
0
2
4
6
8
10
12
9:1
0 A
M
9:1
5 A
M
9:2
0 A
M
9:2
5 A
M
9:3
0 A
M
9:4
0 A
M
9:4
5 A
M
9:5
0 A
M
9:5
5 A
M
# o
f st
ud
en
ts e
nga
ged
in
lect
ure
17 05 11
Series1
Series2
28th March Thermodynamics - Carnot Cycle
Time Group 1 Group 2
9:10 a.m. 10 8
9:15 a.m. 7 9
9:20 a.m. 9 6
9:25 a.m. 9 8
9:30 a.m. 9 9
9:35 a.m. 8 8
9:40 a.m. 9 8
9:45 a.m. 8 7
9:50 a.m. 8 6
4th April Heat Engines, Irreversible Processes
Time Group 1 Group 2
9:10 a.m. 10 10
9:15 a.m. 9 8
9:20 a.m. 9 8
9:25 a.m. 10 7
9:30 a.m. 9 8
9:35 a.m. 10 8
9:40 a.m. 9 7
9:45 a.m. 6 5
7th April Carnot Engines and Heat Engines
9:10 a.m. 7 8
9:15 a.m. 8 8
9:20 a.m. 9 7
9:25 a.m. 10 10
9:30 a.m. 7 6
9:35 a.m. 10 10
9:40 a.m. 7 7
9:45 a.m. 8 9
0
2
4
6
8
10
12
9:1
0 A
M
9:1
5 A
M
9:2
0 A
M
9:2
5 A
M
9:3
0 A
M
9:3
5 A
M
9:4
0 A
M
9:4
5 A
M
9:5
0 A
M #
of
stu
de
nts
en
gage
d in
le
ctu
re
28 03 11
Thermodynamics - Carnot Cycle Group 1
Thermodynamics - Carnot Cycle Group 2
0
2
4
6
8
10
12
9:10 AM
9:15 AM
9:20 AM
9:25 AM
9:30 AM
9:35 AM
9:40 AM
9:45 AM
# o
f st
ud
en
ts e
nga
ged
in
lect
ure
4 04 11
Series1
Series2
Is this the Answer?
47
On-Task # Strategies
20
Class works exercise on own and discuss with neighbour
Worked example on w/board Lecturing on context/importance of information
Slides and demonstration
Worked example on w/board & class input
Explanation of notes demonstration
Demonstration on satellite problem
Clicker problem Admin slides Notes and examples on w/bd video
Demonstration Demonstration contd Demonstration
Lecturing
Explanation of slides Class working on problems Listening to application of new ideas Lecturing
Example Qn from slides Application of concepts Calculation on slides
admin info
Demonstration Worked example on WB Example calculation
Example on WB Definitions on WB Example on WB
Lecturing & notes on WB Lecturing from slides Lecturing & definitions on slides
Lecturing and definitions on slides
19 Lecturing
Example with class input
Historical development
Conceptual problem
Lecturing uniform circular motion Explanation of idea on W/Board Working through printed notes Worked example on bd with class input
Lecturing from slides
Example on WB
Notes on WB Revise solution of hmwk problem
Lecturing on WB
Example on WB & Qs to class
Example on WB
Example on WB
Concepts with demonstration
Is this the Answer?
48
18 Worked example on white bd and class input
Example from notes, worked through on slides and white bd.
Lecturing aspects of calculus from white bd
Lecturing from slides
Example on white bd.
Calculation on white bd
Calculation following printed notes
Example wked out on white bd
Explanation and notes on white bd
Lecturing from slides
Clicker Question
Explanation of graphics on slides
Lecturing from slides
Worked example on slides
Lecturing with slides
Derivation on WB
Wked example hmwk solution
Derivation of Work-energy theorem
Working through assignment type qn on white bd
Derivation of results related to demo
Notes and definitions on WB
Notes contd Lecturing notes on white bd.
Example calculation
Theroem derivation on WB Example on WB
Lecturing and notes on WB
Example on WB Example on WB
Example from text book
Derivation on slides Problems on WB
17 Slides and white board notes
Worked example on white bd
Worked example on white bd
historical account
Neighbour discussion of
demonstration
Example on white bd
Lecturing from slides
Lecturing from slides
Lecturing from slides
Discussion problem
Demonstration
worked problem on slides
Lecturing from slides
Clicker Question
Lecturing from slides and Qn for class
Class example with class input
Explanation of equn from slides
Example on WB to be done as hmwk Calculus based derivation
Example on white bd
Definition of Potential Energy explanation
Example on WB Calculations on WB
Lecturing from slides
Problems from text on WB
Example from text book on WB Definition on slides
Lecturing from slides
Lecturing from slides
Derivation on WB
Derivation on WB
Lecturing from slides
Example contd
Definition on WB
Lecturing & notes on slides
Example of theorem
Is this the Answer?
49
16 Lecturing from slides Lecturing from slides Lecturing & notes on white bd
Worked example on WB Solution of problem on WB Method of dimensional analysis
Revision problems on WB Example contd on WB Lecturing from slides
Lecturing from slides Derivation on WB Demonstration
Lecturing from slides & examples Concepts and definitions from slides
15 Demonstration and printed notes Working from printed notes example on white bd Lecturing from slides
Class problem solving on own Lecturing from slides Lecturing from slides Lecturing on WB
Class problem solving Class discussion of problem Example on WB
Lecturing from slides Lecturing from slides Demonstration and notes on WB
Discussing data from text book Example on WB contd Derivation on slides
Notes and demonstrations Lecturing from slides
14 Lecturing from slides
Using quiz questions
Example for class to work thro'
Lecturing from slides
Derivation of formula on WB
Example done on WB
Lecturing from slides & notes
Example on WB
Demonstration and notes on WB
Lecturing and explaining ideas
Problems on WB
13 Using slides to explain idea Work through problem on slides Demonstration
Lecture on WB Lecturing and definitions on WB Lecturing and definitions on slides
12 Lecturing from WB Derivation on White board Examples on WB Lecture from slides
Problems on WB Calculations on slides Example on WB
11 Examples on WB
10 Lecturing on slides
Is this the Answer?
50
Frequency Table for Each Level of Activity Active Participation Index
20 19 18 17 16 15 14 13 12 11 10
Class works exercise on own and discuss with neighbour 2 0 0 0 0 1 0 0 0 0 0
Clicker problem 1 0 1 1 0 1 0 0 0 0 0
Worked example on w/board & class input 2 3 2 3 0 1 2 1 0 0 0
Explaining idea 3 1 1 1 0 0 0 1 0 0 0
Example Qn from slides, demo, explain applications Exs on WB
14 7 9 11 5 5 3 1 2 0 0
Lecturing; admin; Explanation of slides 12 6 21 20 9 11 7 4 3 1 1
0
5
10
15
20
25
Fre
qu
en
cy o
f o
ccu
rre
nce
of
this
str
ate
gy
Total involvement index = sum of students involved
Frequency vs involvement index from two groups at 10 minute intervals
0
5
10
15
20
25
1 2 3 4 5 6 7 8 9 10 11
Fre
qu
en
cy p
er
22
lect
ure
s
Activity Level
Graph of frequency trend for each Activity Index value
Series2
Series3
Series4
Series5
Series6
Series7
Is this the Answer?
52
Table 1: 2009 Final Grades Achievement Data 2009 Physics 120FC Achievement Report Mark Grade Frequency
Score Frequency %
DNS 19
≥ 50 33.3 D- 23
≥ 49 44.4 D 36
≥ 48
49.2 D+ 26
# students sitting
exam 51.4 C- 21
54.3 C 17 59.3 C+ 46 64.2 B- 30 69.4 B 26 74.3 B+ 18 79.2 A- 18 84.4 A 8 98.8 A+ 22
Total 310
Graph: 2009 Histogram of Grade Frequency vs Grade
0
5
10
15
20
25
30
35
40
45
50
D- D- D+ C- C C+ B- B B+ A- A A+
Freq
uen
cy o
f st
ud
net
ach
ieve
men
t
2009 Frequency of Final Grades
Series1
Is this the Answer?
53
Table 2: 2010 Final Grades Achievement Data 2010 Physics 120FC Achievement Report Mark Grade Frequency
Score Frequency %
DNS
29
≥ 50 150 54.3
D- 26
≥ 49 157 56.9
D 60
≥ 48 161 58.3
D+ 17
# students sitting
exam
C- 17
C 39
C+ 27
Recommended Cut-Off
B- 35
Score No. students %
B 20
45.3 173 62.7
B+ 13
A- 9
A 5
A+ 8
Total 276
Graph: 2010 Histogram of Grade Frequency vs Grade
0
10
20
30
40
50
60
70
D- D D+ C- C C+ B- B B+ A- A A+
Freq
uen
cy o
f St
ud
net
ach
ieve
men
t 2010 Frequency of Grades
Series1
Is this the Answer?
54
Table 3: 2011 Final Grades
Achievement Data
2011 Physics 120FC
Achievement Report
Mark Grade Frequency
Score Frequency %
DNS
32
≥ 50 192 69.3
0 D- 8
≥ 49 201 72.5
29.5 D 24
≥ 48 208 75.1
41.5 D+ 19
# students sitting
exam 277
45 C- 28
49.5 C 36
54.5 C+ 34
Recommended Cut-Off
59.5 B- 34
Score No. students %
64.5 B 27
≥ 48.53 207 74.70%
69.5 B+ 27
74.5 A- 18
79.5 A 13
84.5 A+ 20
Graph: 2011 Histogram of Grade Frequency vs Grade
0
10
20
30
40
D- D D+ C- C C+ B- B B+ A- A A+
2011 Frequency of Grades
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