Assignment 2 (May 5) Derek Yu (UWC) Email:
dyu@uwc.ac.za
1. Introduction
I am currently lecturing ECO211 Basic
Econometrics, ECO311 Intermediate Econometrics and ECO733 Honours Labour
Economics at the Department of Economics, University of the Western Cape (UWC).
In this assignment, I focus on ECO311.
2. Reflection of my teaching practice of
ECO311
Economics as a
discipline used to be highly qualitative. However, the subject has evolved
drastically in the past few decades, and now Economics becomes both highly qualitative
and quantitative. Econometrics, Mathematical Economics and Statistics are involved
to explain and solve economic problems. Also, the students are required to be
proficient in various software packages (Excel, E-Views and Stata) to analyse
the economic data, before the results of the empirical quantitative analysis
are presented to anchor the qualitative arguments. In other words, Economics as
a subject has evolved a lot that it is important the students are provided the
essential teaching and learning support, to obtain the necessary qualitative
and quantitative skills, in order to succeed as an economist when they enter
the labour market one day. The ECO311 module that I focus on in this assignment
is related to the essential quantitative skills the students would learn.
I would start off by
using the constructivist theory by Cohen, Manion & Morrison (2012) and the
‘knowing-acting-being’ theory by Dall’Aba & Barnacle (2007) to explain the
beliefs and values that underlie my teaching. The constructive theory regards
learning as an active process, as the students are encouraged to participate in
learning, instead of passively accepting the knowledge taught in class. Also,
knowledge is constructed rather than received, and learning is continually
developing. As a lecturer, I hope this active learning would take place
successfully by the continuous and strong interaction between the students and
me during the semester.
With regard to the
‘knowing-acting-being’ theory, an Economics lecturer is expected to possess expert
knowledge in his/her area of specialisation (i.e. knowing or subject
knowledge). However, it still does not lead to 100% certainty that I
would be a good lecturer, as I must possess certain key attributes (i.e. being,
or skills and personality). For instance, I
must be hard-working, well-prepared for classes, be available for students
after classes, be able to use various teaching methods in class, strongly
motivate the students, and make students feel I am their buddy to accompany
their learning journey. Finally, I must act as a good lecturer (i.e. doing)
during classes; for example, in addition to the traditional PowerPoint-slides-oriented
conventional teaching method, I apply other teaching techniques to make the
lectures more interesting, interactive and versatile, such as group activities
during classes, application of technology (e.g. IKamva, YouTube) so the
students’ learning would continue even after lectures (i.e. flexible learning).
I also hope the
following relationships
would be developed to improve students’ learning:
·
Interpersonal
relationship: between lecturer and students
- Before lectures
begin (e.g., having the PowerPoint slides of the whole module uploaded on
IKamva in the first week; giving students in-class exercise questions before
lectures)
-
During lectures
(e.g., formal lectures; in-class group exercises)
-
After lectures (e.g.,
consultation hours, e-mails, IKamva, YouTube learning channel)
·
Interpersonal
relationship: amongst the students (e.g., in-class activities; group assignments)
·
Intrapersonal
relationship: the student communicates with himself/herself during the learning
process
ECO211 is the pre-requisite
of ECO311. In ECO211, students already learnt the following:
·
Recap of basic
statistics
·
8-step methodology
of Econometrics
·
12 main assumptions
of the classical linear regression model (CLRM)
·
Difference between
population regression function (PRF) and sample regression function (SRF)
·
Ordinary Least
Squares (OLS) method to derive the sample regression parameters
·
Bivariate
regressions versus multivariate regressions
· Conducting various
statistical tests on the bivariate regression parameters with the application
of the t-distribution and F-distribution
·
Using an elementary
software package (Excel) to conduct econometric analysis
For ECO211, quite a
lot of topics involve “boring”, monotonous and qualitative theoretical content
(e.g. 8-step methodology of Econometrics, assumptions of CLRM, difference
between bivariate and multivariate regressions) – that is, declarative knowledge is taught with the transfer teaching approach, and
it may be really the case that students adopt the surface approach to memorise the theories and describe how to run a regression (that is, relatively low level of
engagement). Also,
for these highly theoretical topics, the relative focus is on the first two
levels of teaching, namely what the student is (information on the abovementioned theories are
displayed to the student) and what the teacher does
(transmitting various key
econometric concepts to the students).
In contrast, the remaining ECO211 topics are more quantitative and
practical in nature (e.g. deriving the regression parameters using the OLS
method; conducting statistical tests on the bivariate regressions; application
of the Excel software) – that is, functioning knowledge is taught with the shaping teaching approach, and students are required to
adopt the deep approach to engage the learning tasks
appropriately to analyse and interpret the regression parameters and explain whether the regression results
conform to economic theories. Also, for these topics, there is stronger focus
on the third level of teaching, namely what the student does, for instance, they attend the
practicals to learn how to use Excel to input the data and then run the
bivariate regressions, before they are required to interpret the regression
results, to ensure that they really understand the content, instead of merely
memorising the theoretical content.
With regard to the
main learning outcomes of ECO311, they are as follows:
·
Recap of OLS method
and assumptions of classical linear regression model (CLRM)
·
Conduct multivariate
regression analysis;
· Conduct various
statistical tests on the multivariate regression parameters with the
application of the t-distribution and F-distribution;
·
Explain the
regressions involving intercept dummy and slope dummy variables, and apply them
to solve economic problems;
· Explain the
definition, consequences and remedies to various violations of the CLRM, namely
multicollinearity, heteroscedasticity and autocorrelation;
· Apply various
statistical tests to detect the presence of multicollinearity,
heteroscedasticity and autocorrelation in the sample regressions;
·
Use an advanced
specialized software package (E-Views) to conduct time-series and
cross-sectional statistical and econometric analysis to solve micro- and macro-economic
problems;
·
Design a
questionnaire to interview a sample of people to obtain data on numerous
variables, before using E-Views to investigate the econometric relationship amongst
these variables and using MS Word to write a research report
From the learning
outcomes of ECO211, it is clear that the students already have some prior knowledge
on the basics of econometrics (this is one of the principles of learning) up
to bivariate regressions (Y is a function of one explanatory variable, X – for
example, consumption is a function of income), but now for the ECO311 students,
they need to go one step further to know how to run multivariate regressions (Y
is a function of at least two explanatory variables – for example, consumption
is a function of income, inflation rate and Rand/US$ exchange rate) and to
conduct various statistical tests on the multivariate regressions. In other
words, for ECO311, I as the lecturer need to prioritise the knowledge and
skills that I need to focus on (this is one of the principles of teaching),
namely multivariate econometric analysis.
Active learning (Bonwell 1991) is involved more in ECO311 when compared
to ECO211, as students act as the main agent, actively involved in doing various
things (e.g. group project – to be discussed later) and thinking about the
things they are doing, to have a deeper understanding of econometrics:
·
Involvement of students: in terms of theoretical
content, most of them have been covered in ECO211, so it means more time would
be available to focus on the practical aspects of econometrics in ECO311 (e.g.
using E-Views to derive multivariate regressions and interpreting the regression
results), and it requires more active involvement of students;
·
Engagement of students: students are required to a
group assignment by interviewing a sample of about 60 people to collect data
relating to an economic topic (e.g. investigating the relationship between
income and consumption), before they input the data on E-Views to run
multivariate regressions and write up a research report to present and
interpret the results;
·
Motivation of students: students’ motivation
increases compared to ECO211, as the students are exposed to the more practical
aspects of econometrics in ECO311;
·
Immediate feedback: although I usually give quick feedback to students
in all the modules I teach, the feedback that ECO311 students receive on the
group project is immediate. That is, once the students are given a topic for
their group project, they are given one week to inform me the explanatory
variables (Xs) they want to include for the multivariate regression, and I
would give them constructive feedback on their proposed econometric model at
the end of the week, before they proceed to interview people to collect data
the following week;
·
Students’ involvement in higher-order thinking: for ECO311, as students no
longer need to spend excessive time on the theoretical aspects of econometrics
as in ECO211, they focus more on conducting econometric analysis, interpreting
the regression results, and explaining whether the results conform to economic
theories.
There is no strong indication of the presence
of obstacles to active learning, because:
·
The volume of work covered in ECO311 is actually less (especially the
theoretical knowledge) when compared to ECO211;
·
There is no indication of excessively long time on class preparation,
other than the fact that some time is needed to record the “how to use E-Views”
videos and upload them on YouTube;
·
The ECO311 class size is smaller than the ECO211 class size, because not
everyone who passed ECO211 continues with Economics Level III. The small class
size of ECO311 hence enables interactive, engaging group activities in class
and in assessment tasks.
·
The E-Views software is purchased by the university, and the university
has a big computer laboratory (with E-Views installed in all computers) for
teaching purpose.
·
Although there could be student resistance to active learning (especially
at the beginning of the semester), I am confident that the explicit guidelines
I put in the in-class exercises, practicals and group assignment would
eventually make students feel comfortable about it.
In ECO211, the highly monotonous, theoretical content is unfortunately
taught primarily with the transfer teaching approach (i.e. conveying information; imparting knowledge; notes of the teacher
becomes notes of the students), and I have to mainly adopt the shaping
teaching approach to do lots of in-class questions
to ensure the students understand how to use the t-distribution and
F-distribution to conduct various statistical tests on the bivariate
regressions (i.e. students’ brains are shaped to a predetermined specification;
exercises all have specific pre-determined outcomes; usual teaching strategy is
that I demonstrate how to solve a problem on the whiteboard before the students
use the same method to solve similar problems).
When students move on to enrol ECO311, they already established strong
foundations on the theories and the application of the t-distribution and
F-distribution on bivariate regressions, so the travelling teaching approach is adopted frequently in
lectures, that is, I would no longer be the main agent to work out the
solutions on the whiteboard, but I would rather play the leadership role to
guide the students and provide suggestions on how to use the same statistical
distributions to conduct various statistical tests on multivariate regressions.
Regarding the E-Views software, unlike Excel, it is not a highly popular
software package to the general public, so after the E-Views practicals, I
upload the “how to use E-Views to conduct econometric analysis” videos on my
YouTube learning channel and I would even upload the E-Views learning manual
(freely provided by the software developer), with the hope that the students would
not only strengthen their basic understanding of E-Views (that is, the topics
covered in the practicals), but they would also be encouraged to do some
self-learning on the advanced E-Views skills that are not covered in class. In
other words, the growing approach is
adopted.
Therefore, compared to ECO211, the focus of ECO311 is on what
the student does by encouraging them to adopt the deep learning approach, as they are more actively
engaged with in-class learning activities, practicals and group assignment. By
involving in meaningful and worthwhile tasks, students not only produce extrinsic motivation (e.g. they could win the “best
ECO311 student award” by working hard), social motivation (e.g. they make their parents happy by doing well in the module) and achievement
motivation (e.g.
outperforming fellow students), but also intrinsic motivation (e.g. feeling self-fulfilled to attain intellectual pleasure by conducting
a practical project to examine and even solve an economic problem to learn functional
knowledge),
under the theory-Y climate – students are given a lot of
freedom in their learning activities and tasks.
Finally, the group assignment is relevant to the SOLO taxonomy to
deepen students’ thinking (I explain this with the aid of one of the research topics
“factors determining the frequency of visiting Facebook per week”):
· Pre-structural: students
don’t understand econometrics at all (this happened until they enrolled ECO211)
· Uni-structural: students
have one idea – there are numerous factors influencing the frequency of someone
visiting Facebook
· Multi-structural: students
have several areas – females could be more likely to visit Facebook; Younger
people would visit Facebook more frequently; those having internet on their
cellular phones would visit Facebook more frequently; whether the person has a
Twitter account could have an impact on his/her frequency of visiting Facebook
·
Relational: Students
use E-Views to run the multivariate regression to analyse the relationship
between the four explanatory variables on the frequency of visiting Facebook,
i.e. frequency of visiting Facebook = β1 + β2Female + β3Age
+ β4Internet Access + β5Having a Twitter account. It is
expected that β2 is positive, β3 is negative, β4
is positive, but β5 could be either positive or negative, according
to theory.
·
Extended
abstract: students use the results of the multivariate regression to critique
and reflect on the relationship between the various social media, e.g. if β5
is positive, it means Twitter and Facebook could be regarded as complements
(i.e. people are likely to be active in both media) but if β5 is
negative, it means Twitter and Facebook could be regarded as substitutes (i.e.
if someone has a Twitter account, he would use Facebook less frequently, as he
may find it annoying and time-consuming to use both social media).
Finally, for this highly practical group assignment, instead of using the
norm-referenced measurement model (as
adopted when marking module test and exam scripts), the criterion-referenced
standards model is adopted to mark the
assignments, with the marking grid focusing on the following four aspects:
questionnaire design (used to interview people to collect data), computer
literacy (evidence that students use E-Views to conduct econometric analysis),
interpretation (of the multivariate regression results) and general
presentation - see Appendix for more detail.
3. Description of students
3.1 Who they are
My ECO311 students first need to pass both Statistics 1 (BUS132) and
Economics 1 (ECO134), before they are eligible to enrol ECO211. Once they pass
ECO211, they are eligible to enrol ECO311. As statistics are intensively covered
in BUS132 and again reviewed in the first three weeks of ECO211, the ECO311
students are very proficient in understanding the basic statistics concepts and
theories, probability distributions, and the application of various
distributions (in particular t-distribution and F-distribution) to conduct
various statistical tests on regressions, such as confidence intervals and
hypothesis testing. In other words, students have decent level of basic
statistical knowledge.
With the exception of few students coming from the part-time group (they prefer
me to provide them the solutions of the in-class exercises), the ECO311
students generally are not the type who want to be spoon-fed. In fact, it is simply
impossible to spoon-feed them: as long as they struggle to build a strong
foundation on statistics (i.e. the BUS132 knowledge) and basic econometrics up
to bivariate regressions (i.e. the ECO211 knowledge), they simply would not be
able to understand the ECO311 intermediate econometrics content that starts
from multivariate regressions.
With regard to the demographic profile of the students, UWC is a
historically black university, and for my ECO311, about 70% of them are
Africans and the remaining 30% are Coloureds or Indians. Interestingly, in the
earlier days I taught this module (in the late 2000s), male students accounted for
the majority of the class, but this no longer happens, as currently half of the
students are females. This is not surprising, as Econometrics has evolved to become
the one of the three core modules (along with Microeconomics and Macroeconomics)
of the Economics curriculum.
3.2 What their needs are
By the time the ECO211
students enrol ECO311 the following year, one thing I immediately notice is
that they no longer clearly remember the OLS methodology to derive regression
parameters and the assumptions of CLRM they learnt in ECO211. This is why the
first learning outcome of ECO311 (as shown in Section 2) is “Recap of OLS
method and assumptions of classical linear regression model (CLRM)”. However,
instead of passively giving a monotonous repeat lecture to refresh their
memory, I rather adopt the travelling teaching approach to lead the students to recall these ECO211 knowledge by means of the
following activities: (1) I spend the first 30 minutes of the lecture to give
them an in-class quiz by asking them to do two questions they already did in
ECO211 that they are required to apply the OLS method to derive regression
parameters; (2) I spend the remaining 30 minutes to give them a second in-class
quiz, by dividing students into groups (a maximum of three students in each
group) to complete a 1-page sheet to explain the 12 assumptions of the CLRM.
In ECO211, for all the regressions discussed in the prescribed chapters,
the independent variables are quantitative variables (e.g. using study hours
and lecture attendance frequency to explain variation in students’ academic
performance). However, in ECO311, students run across dummy-variable
regressions for the first time (the fourth learning outcome of ECO311);
dummy-variable regressions are associated with qualitative, categorical variables
(e.g. gender, race, province of residence) as explanatory variables, and from
my experience all these years, students initially get very confused about the
difference between qualitative and quantitative independent variables as well
as how dummy variables are derived from the qualitative variables. I have
noticed that with the aid of the Excel and E-Views software packages as well as
YouTube learning channel, students would understand the chapter content much
better – this would be explained later.
Talking about E-Views, this is not as highly popular and commonly known
software as Excel. Instead, it is a specialised software package for conducting
intermediate-level statistical and econometric analysis. Even though there are
four E-Views practicals (tutors’ assistance is involved) taking place during
the semester, a lot of students tend to quickly forget about the E-Views
knowledge they learnt in the practicals after one or two weeks. This motivated
me to introduce blended learning since 2014, so that students can learn how to
use E-Views at any time and at place they like (Bates 2016:
214), with the introduction of the YouTube learning channel.
Finally, just like what happened in ECO211, the ECO311 students prefer to
actively do the mathematical and statistical calculations during lectures
(instead of passively waiting for this to happen only in tutorials). For
instance, assuming I have 5 questions in connection with the chapter on
multivariate regressions, I leave 3 questions for the tutors to do in the
tutorials, but for the remaining 2 questions, they are covered in lectures; I
probably would do the first question as an example, but would ask students to
work in groups to do the second question. Of course this approach would mean I
need to have better time management in my lectures, because this means I
allocate about 15 minutes on average in each lecture to cover some tutorial
questions. I believe this approach is necessary, because Econometrics is a
highly quantitative subject (unlike the conventional highly qualitative,
theoretical Economics modules such as Public Economics and Labour Economics)
involving a lot of calculations, so it is important for students to actively do
these practical, mathematical questions during lectures, instead of passively
folding their arms to listen to me talking for an hour and doing the
mathematical questions on the whiteboard all by myself.
To conclude, the four key learning needs of the ECO311 students are:
·
They would need one revision lecture right at the beginning of ECO311 to
recap on the OLS method and assumptions of CLRM;
·
Excel and E-Views software packages are required before students can
understand the content of the dummy-variable regressions chapter better;
·
Blended learning is needed to consolidate students’ understanding of the
specialised E-Views software package for econometric analysis;
·
Students have a strong demand on frequent in-class practical questions,
instead of passively sitting in the lecture venue for an hour to listen to my
lectures and watch me doing all calculations. In other words, students prefer
to have the conventional lectures and tutorial questions being ‘combined’
together as the two key components of each 1-hour lecturing period during the
semester.
4. Departmental, institutional, and
socio-economic context
4.1 Departmental context
Until 2013, the Department of Economics only offered one undergraduate
Econometrics module, namely ECO311. However, this “old” ECO311 module was
associated with a very fast and hectic teaching schedule, covering 13 chapters
of the prescribed textbook (Gujarati 2009: Basic Econometrics). In particular,
it was not possible for me to cover the chapters on violations of CLRM (refer
to the fifth and sixth learning outcomes in Section 2) in big detail at that
time.
It was only from 2014 that the Department decided to split the “old”
ECO311 into two modules, namely ECO211 and the current “new” ECO311. As mentioned
earlier, the easier topics are covered in ECO211 while the intermediate-level
topics are rather covered in the current “new” ECO311. The pass rates of both modules
have been very high at above 90%, with positive feedback received from the
students in the annual evaluation of the two modules.
4.2 Institutional context
Due to the increasing importance of Econometrics, it was approved by the
university at the end of 2013 that, from 2014, the BCom and BAdmin curricula
are revised so that ECO211 has become a new Economics elective module at Level
2, along with Labour Economics (ECO233), Public Economics (ECO234) and Mathematical
Economics (ECO235). At this level, students who want to continue with Economics
are required to enrol four Economics modules – Microeconomics (ECO231) and
Macroeconomics (ECO232) are compulsory, and students need to choose any two of ECO211,
ECO233, ECO234 and ECO235.
The Economics Department later found that it may not be wise to have
ECO211 and ECO235 as electives, as it may indirectly lead to a situation that
some students who have personal rejection against quantitative economics would
avoid enrolling ECO211 and ECO235 (but rather opt to enrol ECO233 and ECO234).
Therefore, from 2018, a new BCom program would be offered, and at Level 2,
ECO231, ECO232, ECO211 and ECO235 would become the four compulsory Economics
modules. Labour Economics and Public Economics would rather move upwards to
become Level 3 modules.
4.3 Socio-economic context
It was already mentioned earlier that it is now typical for economists in
the real world of work to be asked to analyse economic data (e.g. from South
African Reserve Bank and Statistics South Africa), so it is now extremely
important for students to have done some courses in Econometrics. In fact, UWC Economics
Department has a comparative advantage in this regard, as it is the only
Economics Department in South Africa offering two undergraduate Econometrics
courses. In other words, our undergraduate students learnt more Econometrics knowledge
than any other students from institutions other than UWC.
5. Constructive alignment of module
framework
I believe constructive alignment of the ECO311 framework should show
strong connection between the following key components, namely learning
outcomes, course content, teaching and learning (T&L) activities,
assessment tasks, graduate attributes, regular feedback from the lecturer and
feedback from students. Each component is explained clearly below.
·
Learning
outcomes: they
need to be clearly stated in the course outline and module descriptor (refer to
the eight learning outcomes of ECO311 in Section 2). As the lecturer, I need to
ensure that all learning outcomes are achieved, regardless of whether the
students eventually pass the module at the end.
·
Course
content (Topics): if I waste excessive time to recap the easier chapters that were
already covered in ECO211 and/or omit the essential intermediate-level
chapters, it would not be possible to enrich students’ econometrics knowledge
as the course would not be up to standard, not to say the achievement of
learning outcomes.
·
Teaching and
learning activities: in addition to lectures and tutorials, I need to adopt various
approaches to cater for the highly quantitative nature of this module, e.g.
blended learning (using YouTube learning channel), students working in groups
to solve econometric problems in class, case studies (e.g. using economic data
from Statistics South Africa and South African Reserve Bank to explain
real-life econometric relationship between independent and dependent variables).
·
Assessment tasks: other than giving class
tests, module test and final exams (that focus on testing students’
understanding of the advanced chapters on the violations of the CLRM and
dummy-variable regressions, as well as the mathematical calculations involving
t-distribution and F-distribution) to students, they are required to do a group
assignment to interview a sample of people, before compiling data to use
E-Views to analyse the data to write a report to explain real-life economic problems.
This assignment is crucial to make sure students are able to conduct a
practical, econometrics project, upon passing the ECO311 module, as they would
be expected to do a similar project when they work as economists one day.
·
Graduate
attributes: in
addition to learn the essential econometrics knowledge from the prescribed
textbook, the other graduate attributes I would like students to get upon
finishing the ECO311 module are: (1) be able to work independently (e.g. tests
and exams) and with others as a group (e.g. group assignment); (2) engaging
people from different backgrounds (e.g. group assignment by working with
students from other gender and population groups; interacting people who are
not students when interviewing a sample of people to collect data for the
assignment); (3) be skilled communicators (using E-Views and MS Word to conduct
a highly-practical econometrics project); (4) ethically, environmentally and
socially aware and active (e.g. students should know it is wrong to dishonestly
commit plagiarism or fabricate the data and regression results in the group
assignment; it is also not right to force certain people to be interviewed);
(5) inquiry-focused and knowledgeable (students could apply the econometrics
knowledge they learn to investigate any real-life economic problems to find out
the relationship between the variables, assuming data on these variables is
available).
·
Feedback from
lecturer: it is
important to give students feedback regularly; while given the time constraint
in class, I would verbally give them a 5-minute feedback after they write each
test, but I would upload a comprehensive 1-page feedback document on the course
e-learning site. I also allocate one week in the first week of the second term
to allow students to see me to obtain feedback on the questionnaire they
designed for the group assignment, before they proceed to use the questionnaire
to collect data.
·
Feedback from
students: I
hand out an evaluation form to the students at the beginning of the second
term, to collect feedback from them on the standard of the course, whether they
think the learning outcomes are achieved, whether I use versatile teaching and
learning approaches, and whether the assessment activities are strongly linked
to the course content, learning outcomes and graduate attributes.
Learning outcome
|
Topic
|
T&L Activities
|
Assess-ment
|
Graduate attributes#
|
Recap of OLS method and assumptions of classical linear regression
model (CLRM)
|
(Recap of) Chapter 1: Nature of regression analysis
(Recap of) Chapter 4: Classical linear regression model
|
Lectures
(1 week)
|
In-class quiz
|
[II]
[III]
[IV]
[VI]
|
Conduct multivariate regression analysis
|
Chapter 7: Multivariate regression analysis
|
Lectures
Tutorials
(2 weeks)
|
Module test
Exam
|
[II]
[III]
[IV]
[VI]
|
Conduct various statistical tests on the multivariate regression
parameters with the application of the t-distribution and F-distribution
|
Chapter 8: Multivariate regression analysis: problem of estimation and
inference
|
Lectures
Tutorials
(2 weeks)
|
Module test
Exam
|
[II]
[III]
[IV]
[VI]
|
Explain the regressions involving intercept dummy and slope dummy
variables, and apply them to solve economic problems
|
Chapter 9: Dummy variable regression models
|
Lectures
Tutorials
Practicals
Blending learning
(2 weeks)
|
Module test
Exam
|
[II]
[III]
[IV]
[VI]
|
Explain the
definition, consequences and remedies to various violations of the CLRM:
multicollinearity, heteroscedasticity and autocorrelation
|
Chapter 10: Multicollinearity
Chapter 11: Heteroscedasticity
Chapter 12: Autocorrelation
|
Lectures
Tutorials
Practicals
(2.5 week)
|
Module test
Exam
|
[II]
[III]
[IV]
[VI]
|
Apply various statistical
tests to detect the presence of multicollinearity, heterosce-dasticity and
autocorrelation in the sample regressions
|
Chapter 10: Multicollinearity
Chapter 11: Heteroscedasticity
Chapter 12: Autocorrelation
|
Lectures
Tutorials
Practicals
(2.5 weeks)
|
Module test
Exam
|
[II]
[III]
[IV]
[VI]
|
Design a
questionnaire to interview a sample of people to obtain data on numerous
variables, before using E-Views to investigate the econometric relationship
amongst these variables and using MS Word to write a research report
|
Appendix: Carrying out an empirical project
|
Lectures
Tutorials
Practicals
Blending learning
(1 weeks)
|
Group assignment
|
[I]
[II]
[III]
[IV]
[V]
[VI]
|
[II]: Inquiry-focused and
knowledgeable
[III]: Critically relevant and
literate
[IV]: Autonomous and
collaborative
[V]: Ethically, environmentally
and socially aware and active
[VI]: Skilled communicators
6. Threshold concepts and blended
learning activities
6.1 Two threshold concepts of ECO311
Two out of four key learning needs of the students as mentioned in
Section 3.2 are threshold concepts that require blending learning activities.
·
Students would
better understand the chapter on dummy-variable regressions with the aid of
Excel, E-Views and YouTube learning channel.
·
Students tend to
quickly forget about the E-Views skills learnt in the practicals, so it is
important to give them an opportunity to ‘attend’ the practicals again at any
place and time that suit them, by watching the videos on the YouTube learning
channel.
6.2 How to address the threshold concepts
With regard to the first threshold (dummy-variable regressions), by
adopting the follow three-step approach, I notice that students eventually
succeed in understanding the chapter content:
·
Explain the linkage between qualitative variables and dummy variables, as
well as derivation of dummy variables, with the aid of MS Excel;
·
Demonstrate how to run regressions that involve dummy variables with the
aid of E-Views;
·
Upload the MS Excel and E-Views demonstration video on the ECO311 YouTube
learning channel, so that students can have unlimited opportunities to go
through the teaching and learning activities in connection with this chapter
With regard to the second threshold (How to use E-Views software), in addition to the four E-Views practicals presented by the tutor during the semester, I uploaded the six E-Views demonstration videos on the ECO311 YouTube learning channels, covering the following topics:
* Basic data analysis;
* Econometric analysis;
* Using E-Views to answer the questions of practical #1;
* Using E-Views to answer the questions of practical #2;
* Using E-Views to answer the questions of practical #3.
Finally, the next page onwards shows how I use Learning Designer to
design my lectures on dummy-variables regressions and practicals on E-Views respectively.
Learning Design for: Dummy-variable
regressions
(Exported from Learning Designer)
Context
Topic: Dummy-variable regression models
Total learning time: 240
Number of students: 30
Description: 4-hour lectures (2 lectures per week * 2 weeks) would
take place to cover Chapter 9: dummy-variable regression models
Aims
To conduct econometric analysis
that involves both quantitative and qualitative (dummy) variables as
independent variables
Outcomes
Application (Application):
Explain the regressions involving intercept dummy and slope dummy variables,
and apply them to solve economic problems
Teaching-Learning
activities
Nature of dummy variables
Discuss 30
minutes students Tutor is available
To explain the nature of dummy
variables
* Difference between quantitative
and qualitative variables
* Relationship between
categorical variables and dummy variables
* Derivation of dummy variables
ANOVA and ANCOVA models
Discuss 60
minutes students Tutor is available
To explain the ANOVA and ANCOVA
models that both involve dummy variables as independent variables
* ANOVA models
* ANCOVA models
Piecewise linear regression models
Discuss 30
minutes students Tutor is available
Explain how dummy variables are
involved to run piecewise linear regression models to explain change of slope
of relationship between the independent variable (X) and dependent variable (Y)
from a particular threshold (X*)
Using Excel and E-Views to conduct dummy-variable regressions
Practice 60
minutes students Tutor is available
To conduct econometric analysis
involving dummy variables with the aid of Excel and E-Views
* Creation of dummy variables on
Excel
* Creation of E-Views work files
that involve dummy variables
* Graphical plot to examine the
relationship between dummy variables and dependent variables
* Run regressions that contain
dummy variables as independent variable(s)
* Interpret the regression
results
* ECO311 YouTube learning channel
video in connection with dummy-variable regression models
YouTube learning channels
Collaborate 60 minutes students Tutor
is available
Students work in pairs to create
an E-Views file to conduct econometric analysis that involve dummy variables as
independent variables, and interpret the results. This 1-hour lecture would
take place in the computer laboratory to cater for students who do not have
their own laptop computers.
Learning Design for: E-Views software
(Exported from Learning Designer)
Context
Topic: Application of the E-Views software
Total learning time: 120
Number of students: 30
Description: A 2-hour practical will take place in a computer
laboratory (with the involvement of tutors) for students to learn how to use
E-Views to conduct econometric analysis.
Aims
To use an advanced specialised
software package (E-Views) to conduct econometric analysis
Outcomes
Application: To apply the E-Views
software package to conduct econometric analysis.
Teaching-Learning
activities
Basic data management skills
Produce 30
minutes students Tutor is available
To create an E-Views data file by
mastering the following skills:
* Import data from Excel
* Copy the data from Excel and
paste it onto E-Views spreadsheets
* Generate new variables by
manually typing the data
* Generate new variables by
typing equations
Basic statistical analysis
Practice 30
minutes students Tutor is available
To conduct basic statistical
analysis of the data:
* Derive mean, variance and
standard deviation
* Correlation coefficients
* Bar charts, line charts and XY
scatter plot
Econometric analysis
Practice 30
minutes students Tutor is available
To conduct econometric analysis:
* Bivariate regressions
* Multivariate regressions
* Derive residuals and predicted
y-values
Interpretation of regressions
Discuss 25
minutes students Tutor is available
To interpret the regression
results:
* The constant and slope
parameters
* R-squared, adjusted R-squared
* T-statistics
* F-statistics
YouTube learning channels
Discuss 5
minutes students Tutor is available
To introduce the YouTube learning
channel to students, so that in case they don't remember the E-Views skills
they learnt during the practicals, they could always watch the videos on the
channel at any place and any time that suit them.
Appendix: Criterion-reference grid for
the ECO311 group project
