如何把偶然扭转成必然？

偶然，可以是好的，也可以是不好的。

好的偶然英文叫serendipity，一般上被翻译为正面的意外。
不好的偶然在英语世界里和意外同义，叫accident。

要把偶然扭转成必然，这里头的前提是把偶然当作是件坏事，不然就不需要“扭转”。因为“扭转”一般上是把不好的事物变成好的。以此推演的话，“如何把偶然扭转成必然？”就是：How to turn accidentality into inevitability?

如果所谓的偶然实际上是正面的事物，那么“扭转”这个负面的动词就得由中性的“变”替代。
这样，“如何把偶然变成必然？”就是：How to turn serendipity into inevitability?

——————————
“如何把偶然扭转成必然？”这句话是我在看介绍日本著名男声优松岗祯丞的优管视频时听到的。松岗祯丞患有社交恐惧症、女性恐惧症和摄像头恐惧症，但是却成功地把自己面对的问题扭转成可以让他成名的声优事业。

我听到这句话时，想到的是怎么把Robest的偶然性发明变成有一定规律话必然性的发明。

Congratulations! Dr Shahrel Nizar Baharom

After slightly more than three years of hard working days and sleepless nights, my PhD student, Mr. Shahrel Nizar, a lecturer from UiTM is now Dr. Shahrel Nizar. On 14 July 2017, he passed his viva voce with minor correction.

Throughout his doctoral study, he went through numerous difficulties and challenges at his research work and personal life, but he overcame all of them, successfully. Well done Shahrel!

His success taught me a lesson:

Pursuing a PhD is not only about our IQ, it involves high EQ and consistent positive attitude as well.

I became a PhD supervisor in 2012. The first PhD student assigned to me chose to withdraw from her study after the orientation day. Thus, there was more failure than success stories. In fact, not all registered doctoral students can make to the end of the journey, but I think this low success rate would make those who are now successful more appreciated.

At heart, I am also learning as a supervisor. As my knowledge, skills and experience grow over time, I become more particular and selective when potential postgraduate students approach me, telling me they wish to be supervised by me. Like my late mentor, Assoc Prof Dr Stanley Richardson always told me in the past:

The first principle of war is the selection and maintenance of the aim. And the aim in research is the one indispensable results plus constraints. (Richardson, 2005). 

To set the right aim, we do need IQ. However, to maintain the aim over several years with a lot of unpredictable challenges at work and in life, we really need high EQ.

Now I understand why Stanley would spend quite some time with a potential PhD student, understanding his cognitive strengths and weaknesses, and more importantly, observing his attitudes and behaviours towards academic research, before accepting or rejecting the student. It was not about how well or how thick their PhD research proposals were written, it was really about whether they would bear the temptation of giving up their research at any point of a research timeline.

Shahrel's success is also an outcome of his disciplined behaviour and consistent attitude towards the research plan and timeline we both set and adjusted along his doctoral research journey. His success motivates me to supervise more dedicated students like him to achieve their success. At heart, this reminds me of my typical answer to "how to research":

Do the right thing at the right time and use the right method. 

I wish all my other postgraduate students can refer to Shahrel as a role model, especially his respect and commitment on his own research plan and timeline. Brovo Dr Shahrel Nizar!!

Reading Notes: Statistics 101 (Part 4)

Data Analysis

Two ways:
- Looking at the data graphically to see what the general trends in the data are, and - Fitting statistical models to the data.

Frequency distribution: a graph plotting values of observations on the horizontal axis, and the frequency with which each value occurs in the data set on the vertical axis (a.k.a. histogram).

Reading Notes: Statistics 101 (Part 3)

Data collection methods in experiment research
1. to manipulate the independent variable using different entities.
== a between-groups, between-subjects, or independent design.
2. to manipulate the independent variable using the same entities.
== this means that giving a group of students positive reinforcement for a few weeks and test their statistical abilities and then begin to give this same group punishment for a few weeks before testing them again, and then finally give them no motivator and test them for a third time.
== a within-subject or repeated-measures design.

Data collection method determines the type of test that is used to analyse the data.

Andy Field:
The reason why some people think that certain statistical tests allow causal inferences is that historically certain tests (e.g., ANOVA, t-tests, etc.) have been used to analyse experimental research, whereas others (e.g., regression, correlation) have been used to analyse correlational research (Cronbach, 1957)...these statistical procedures are, in fact, mathematically identical.

Two sources of variation:
Systematic variation: This variation is due to the experimenter doing something in one condition but not in the other condition.
Unsystematic variation: This variation results from random factors that exist between the experimental conditions (such as natural differences in ability, the time of day, etc.).

In a repeated-measures design, differences between two conditions can be caused by only two things:
(1) the manipulation that was carried out on the participants, or
(2) any other factor that might affect the way in which an entity performs from one time to the next.
== The latter factor is likely to be fairly minor compared to the influence of the experimental manipulation.

In an independent design, differences between the two conditions can also be caused by one of two things:
(1) the manipulation that was carried out on the participants, or
(2) differences between the characteristics of the entities allocated to each of the groups.
== The latter factor in this instance is likely to create considerable random variation both within each condition and between them.

When we look at the effect of our experimental manipulation, it is always against a background of ‘noise’ caused by random, uncontrollable differences between our conditions.

In a repeated-measures design this ‘noise’ is kept to a minimum and so the effect of the experiment is more likely to show up.

This means that, other things being equal, repeated-measures designs have more power to detect effects than independent designs.

The two most important sources of systematic variation in repeated-measures design are:
Practice effects: Participants may perform differently in the second condition because of familiarity with the experimental situation and/or the measures being used.

Boredom effects: Participants may perform differently in the second condition because they are tired or bored from having completed the first condition.

Randomization: the process of doing things in an unsystematic or random way. In the context of experimental research the word usually applies to the random assignment of participants to different treatment conditions.

Reading Notes: Statistics 101 (Part 2)

Source: Andy Field
Correlational or cross-sectional research: observe what naturally goes on in the world without directly interfering with it.
- by either taking a snapshot of many variables at a single point in time, or
- by measuring variables repeatedly at different time points (known as longitudinal research).
== provides a very natural view of the question we’re researching because we are not influencing what happens and the measures of the variables should not be biased by the researcher being there (an important aspect of ecological validity).
== tells us nothing about the causal influence of variables.
- Variables are often measured simultaneously.
- The first problem with doing this is that it provides no information about the contiguity between different variables.
- The second problem with correlational evidence: the tertium quid (‘a third person or thing of indeterminate character’).
== E.g., a correlation has been found between having breast implants and suicide (Koot, Peeters, Granath, Grobbee, & Nyren, 2003).
== However, it is unlikely that having breast implants causes you to commit suicide – presumably, there is an external factor (or factors) that causes both; for example, low self-esteem might lead you to have breast implants and also attempt suicide.
== These extraneous factors are sometimes called confounding variables or confounds for short.

Experimental research: manipulate one variable to see its effect on another.
- Even when the cause–effect relationship is not explicitly stated, most research questions can be broken down into a proposed cause and a proposed outcome.
- Both the cause and the outcome are variables.
- The key to answering the research question is to uncover how the proposed cause and the proposed outcome relate to each other.

David Hume said that to infer cause and effect:
(1) cause and effect must occur close together in time (contiguity);
(2) the cause must occur before an effect does; and
(3) the effect should never occur without the presence of the cause.

- These conditions imply that causality can be inferred through corroborating evidence: cause is equated to high degrees of correlation between contiguous events.

- The shortcomings of Hume’s criteria led John Stuart Mill (1865) to add a further criterion: that all other explanations of the cause–effect relationship be ruled out.
== Mill proposed that, to rule out confounding variables, an effect should be present when the cause is present and that when the cause is absent the effect should be absent also.
== Mill’s ideas can be summed up by saying that the only way to infer causality is through comparison of two controlled situations: one in which the cause is present and one in which the cause is absent.

- This is what experimental methods strive to do: to provide a comparison of situations (usually called treatments or conditions) in which the proposed cause is present or absent.
- Example: the effect of motivators on learning about statistics. Randomly split some students into three different groups in which teaching styles vary in the seminars:
== Group 1 (positive reinforcement): praise participants
== Group 2 (punishment): give verbal punishment
== Group 3 (no motivator): give neither praise or punishment, i.e. give no feedback at all.

Manipulated variable or independent variable: the motivator (positive reinforcement, punishment or no motivator).
Interested outcome or dependent variable: statistical ability, to be measured via a statistics exam after the last seminar.
Assumption: the scores will depend upon the type of teaching method used (the independent variable).
Inclusion of the ‘no motivator’ group: proposed cause (motivator) is absent, and we can compare the outcome in this group against the two situations in which the proposed cause is present.

If the statistics scores are different in each of the motivation groups (cause is present) compared to the group for which no motivator was given (cause is absent) then this difference can be attributed to the type of motivator used.
In other words, the motivator used caused a difference in statistics scores.

Proposal Defence

The key problem faced by my students in Proposal Defence sessions was the lack of defence.

When they were questioned or challenged, they noded their heads, accepting the comments and doubts held by panel examiners, as opposed to argue with the examiners and clarify doubts.

Another problem they normally had was the lack of coherence, esp between the problem statement, research objectives and research questions.

For those who don't have learning experience in foundation research course, i.e. having q Master's degree by completing course work instead of research project, this would make then having serious problem in research.

Reading notes: Statistics 101 (Part 1)

Learning from Andy Field, on my daily travelling between Jurong East MRT Station and Expo MRT Station

Overview
From an initial observation, explanations, or theories are generated for those observations, from which predictions (hypotheses) can be made. This is where the data come into the process because to test those predictions data are needed.
- First, collect some relevant data (i.e. identify things that can be measured) and then analyse those data.
- The analysis of the data may support the theory or give the cause to modify the theory.
- As such, the processes of data collection and analysis and generating theories are intrinsically linked: theories lead to data collection/analysis and data collection/analysis informs theories.

In the process of generating theories and hypotheses, data are important for testing hypotheses or deciding between competing theories. In essence, two things need to be decided: (1) what to measure, and (2) how to measure it.

To test hypotheses we need to measure variables.
Variables are just things that can change (or vary); they might vary between people (e.g., IQ, behaviour) or locations (e.g., unemployment) or even time (e.g., mood, profit, number of cancerous cells).

The key to testing scientific statements is to measure a proposed cause (the independent variable) and a proposed outcome (the dependent variable).

Independent variable: A variable thought to be the cause of some effect. This term is usually used in experimental research to denote a variable that the experimenter has manipulated.
== Predictor variable: A variable thought to predict an outcome variable. This is basically another term for independent variable .

Dependent variable: A variable thought to be affected by changes in an independent variable. You can think of this variable as an outcome.
==Outcome variable: A variable thought to change as a function of changes in a predictor variable; aka dependent variable.

Levels of measurement
Variables can be split into categorical and continuous, and within these types there are different levels of measurement:
1. Categorical (entities are divided into distinct categories):
1.1 Binary variable: There are only two categories (e.g., dead or alive).
1.2 Nominal variable: There are more than two categories (e.g., whether someone is an omnivore, vegetarian, vegan, or fruitarian).
1.3 Ordinal variable: The same as a nominal variable but the categories have a logical order (e.g., whether people got a fail, a pass, a merit or a distinction in their exam).

2. Continuous (entities get a distinct score):
2.1 Interval variable: Equal intervals on the variable represent equal differences in the property being measured (e.g., the difference between 6 and 8 is equivalent to the difference between 13 and 15).
2.2 Ratio variable: The same as an interval variable, but the ratios of scores on the scale must also make sense (e.g., a score of 16 on an anxiety scale means that the person is, in reality, twice as anxious as someone scoring 8).

Two measurement-related issues
1. Standard units of measurement
2. Difference in results between studies.

One way to try to ensure that measurement error is kept to a minimum is to determine properties of the measure (validity and reliability) that give us confidence that it is doing its job properly.
Validity: whether an instrument actually measures what it sets out to measure. Reliability: whether an instrument can be interpreted consistently across different situations.

Criterion validity: whether an instrument measures what it claims to measure through comparison to objective criteria.
- In an ideal world, you assess this by relating scores on your measure to real-world observations.

== Concurrent validity: a form of criterion validity where there is evidence that scores from an instrument correspond to concurrently recorded external measures conceptually related to the measured construct.

== Predictive validity: a form of criterion validity where there is evidence that scores from an instrument predict external measures (recorded at a different point in time) conceptually related to the measured construct.

- Assessing criterion validity (whether concurrently or predictively) is often impractical because objective criteria that can be measured easily may not exist.
- With attitudes it might be the person’s perception of reality rather than reality itself that you’re interested in.

Content validity: evidence that the content of a test corresponds to the content of the construct it was designed to cover.

Validity is a necessary but not sufficient condition of a measure.
A second consideration is reliability, which is the ability of the measure to produce the same results under the same conditions.
To be valid the instrument must first be reliable.
The easiest way to assess reliability is to test the same group of people twice: a reliable instrument will produce similar scores at both points in time (test–retest reliability).
Sometimes, however, you will want to measure something that does vary over time.

Experiences of applying for IRB

In my PhD, Warwick University had established a rigorous process and standard for ethical approval in educational studies. So applying for the approval is a norm and nobody thinks it as a burden.

However, when I returned to Malaysia where IRB application was not there, I tried to retain the practice and attitude for ethics in education research.

Now when I am working in SUTD, I found academics and staff are scared by the tedious process and revision needed in applying IRB. The process in Singapore institutions is indeed more complicated. And people who don't see the benefits of doing it felt frustrated with the process and rejection.

This prompted the thought of challenges faced if UPSI is going to impose IRB. We need a dedicated and strict team to implement this.

Talk by the VC of Cambridge University

Title: Universities and Their Role in an Era of Global Challenges

Delivered by Professor Sir Leszek Borysiewicz FRS, FMedSci, the Vice Chancellor of Cambridge University

Organiser: Jeffrey Cheah Distinguished Speakers Series (JCDSS) Sunway University

The role of universities in an era of global challenges, an era where expertise itself is fall into question. 

What does it is actually mean in this day of age to be a world-class university?

League tables are too simplistic...it does not show the depth of any academic institution has to deliver. 

A university is global when it reflects global diversity, addresses global issues; when it establishes global partnership; and when it assumes global leadership. 

The diversity is the key.

Cambridge is suffering in offering courses via MOOC to cope with the global change.

Cambridge is facing immense challenges in replicating the colleagial ambience in physical unversity outside of Cambridge.

"We are global because we address global issues."
... Looking for practical solutions to issues we face.

It has to become the daily work of academics.

World class university must harness... collaboration.

Strategic partnership
Collaboration network
Retain collaboration at all costs.

Global leadership requires courage, creativity and cooperation.

Global diversity addressing global issues...

Failure to address these issues would leave universities out.

Trust gap between the uninformed  and the academic experts is alarming.

When expert is now a term of abuse.

It's damaging to our reputation...

We need the experts
Let's continue
Engage the wider society
Stand up and be heard
Strike to be more transparent...
Make no apology for it at all.
Keep doing what we can do best.
Doing so will not make us popular

Be sincere.

To contribute to the society ...
If we do not serve the society, the global society,

It is the yardstick....

How to develop a framework

One way of developing framework: come out with a framework without obvious methodogic approach. For example, Prof Dr Badrul Huda Khan said his E-learning Framework was not based or grounded on any research, instead from listening and understanding the needs of students.
The framework becomes highly recommended and recognized, and then he wrote books about the model.

Another way of developing a model or framework is like doing the process-based design of NRGS Programme led by UPSI.

To me and my postgraduate students, herewith what I suggest for developing a  framework:
1. Review literature on issues and key concepts associated that may form a framework.
2. Develop provisional guiding principles for ideal practice.
3. Structure a provisional framework based on provisional guiding principles.
4. Define all key concepts used in the provisional framework.
5. Design and develop instrument(s) to validate the framework.
6. Validate the instrument(s)
7. Validate the framework through empirical studies using the validated instrument(s)

Notes taken from "Meaningful E-Learning in Education"

Learning is the main thing. "E" is the technology, the sidekick not the main thing.

"I cannot live without sarung...
I bring the flavor of Bangla to America."
"Thinking globally, acting locally."

I asked, "What is non-meaningful e-learning?"
Non-meaningful e-learning means students do not get what suppose to get from learning.

New book: Data Analytics in Education

Agenda:
1. People Process Product Continuum.
2. Stakeholders' needs
3. E-learning framework

"E-learning is an instructional model that allows instructor, students, and content to be located in different noncentralized locations so that instruction and learning occur independent of time and place."

"E-learning is distributed".

"I am not the technology guy, I am just the messager."

"Tools here are going to jeopardy your life, you have to decide whether the tools are supplementing or empowering you or not."

Instructional designer is like an architect, who will come out with the blueprint of instruction.

A high quality e-learning system must be meaningful to stakeholders. It is more likely to be meaningful to learners when it is:
- easily accessible
- clearly organized
- well written
- authoritatively presented (it is something educational)
- learner-centered
- affordable
- efficient
- flexible, and has a
- facilitated learning environment.

"Maybe some of them aren't even ready for the lecture."

A Thai student asked, "Is University going to stay?"

"When passion becomes profession, life is no longer boring."

Try to make your profession your passion, then you enjoy what you are doing.

Don't do e-learning unless you are ready.

When learners display a high level of participation and success in meeting courses goals...

When learners enjoy all available support services provided in the course without any interruptions, it makes support services staff happy as...

Finally, e-learning is meaningful to an organization when it has a sound  ROI, a moderate to high level of learners' satisfaction...

Today, I would like to introduce a framework which I believe will help us identify important criteria for quality e-learning and blended learning.

Framework of E-learning
http://bookstoread.com/framework/scroller.htm

Learning arts systematically? How I survived back then.

When I entred MMU in 1999, I chose a wrong programme.
However, I took the challenge to rectify my mistake, as an appreciation of getting PTPTN from the government and as an opportunity to obtain a Bachelor degree.

The first problem I faced when choosing a wrong programme was the need to master drawing skills. To be honest, drawing was not my cup of tea at that moment, and I did not have interest to learn drawing. However, for the sake of survival, I treated drawing as a professional work systematically.

I established a systematic learning sequence for arts: definition, incubation, illumination and verification.

Step 1: Definition
Before entering a class, I do pre-lesson learning by clarifying meanings of all jargons used in the lesson. I believed I need to know exactly what I need to do when I was given instructions to draw something. Thanks to internet, I managed to get definitions of all art movement, e.g. pointillism, expressionism, realism, photo-realism, Art Nouveau, Art Deco, etc. I also made to best of the visit to National Art Gallery (now National Visual Arts Gallery) in KL. I kept asking questions in the class, and seeking for meanings of particular art movement and style in MMU Library.
With sufficient knowledge and understanding on what jargon actually means, I could at least talk about arts--without a mastery of creating them.

Step 2: Incubation
To produce quality drawing requires incubation time. This would be the time spent to establish the ambience for inspiration and generation of ideas. Preparation to draw, like setting up the canvas, mixing colours, washing brushes, sharpening pencils or charcoal, etc, can be regarded as incubation as well. I also built up a habit to listen to New Age music during this period of my life. I particularly like Enya's music.

Step 3: Illumination
I started drawing by sketching what I had in mind without worrying the beauty of the sketches of work or not. After multiple attempts, I would proceed to the illumination stage, when I began to fill in colours, lighting, and life into the sketches. Static drawing or sketches turned animated in my mind. I began to think and express what I thought through illumination on the drawing.

Step 4: Verification
After getting a few versions of drawing done, I spent time framing them accurately according to the specifications set by lecturers. Then I brought all the framed works to the faculty and got hold of my lecturers to comment on and verify their quality. The chosen or preferred version would be considered as verified version. I moved on to finalise this verified version, adding values to the work and submit on the due date.

-----------------

Learning and re-learning drawing in MMU in 1999 and 2000 was indeed a painful experience in my life. However, during those years, I tried convincing myself: even if I cannot draw now, but I can learn to draw! And I realised most of my course mates also cannot draw like me. Those who actually can draw, among my peers, I can actually count by hand. I did think of changing faculty, to IT or Management, but that would lead to two problems:

a) financial problem: I already spent money buying drawing materials, tools, etc. The study loan was approved and changing programme would lead to chaotic situations, and I did not actually have money to repay fee in the new programme.

b) curriculum: I was not scoring very well in physics, add maths, etc, and I might not be able to score flying colours in IT or science-oriented programmes.

So to master drawing, I thought of following the footstep of Leonardo Da Vinci, get a Master and learn from him persistently until I succeed. To cover the weakness of drawing, I will work harder on non-drawing subject matters, hoping that I could score in those subjects to compensate. I managed to establish good discipline, i.e. spending most time learning and practice instead of going out the campus, like my peers did.

In fact, I was reaching the end of the journey, it would not be worthwhile to give up at that moment.

Setting the functions of workspace

After resuming non-admin working life, I started to sort out things I planned years ago. One of them was to re-establishing a conducive work space in UPSI.

When I was the Director of UERL, I split my working life into two--one in the admin office at the Sultan Azlan Shah Campus, another in FSKIK. Now, I finally managed to merge them into one. Also, after becoming a father, my work space at home turned chaos because I have to reset the positions of most of my stuff to avoid being ruined by Thales or being hidden somewhere in the house. 

To sort out things for a conducive workspace, I would start tagging all materials in the office according to their functions:

1. Research and Development (R&D)

1.1. Universiti Research Projects

1.2 MyGrants 

1.3 External grants

2. Publication

2.1 Book & Book Chapters

2.2 Journal article & journal management

2.3 Conference papers 

2.4 Commission writing project

2.5 News Articles 

3. Supervision of students

3.1 Postgraduate student supervision

3.2 Undergraduate student supervision

3.3 Intern supervision

4. Teaching

4.1 Undergraduate teaching

4.2 Diploma teaching 

4.3 Exam papers 

5. Consultation

5.1 Paid consultation projects

5.2 Pro bono projects

6. Community Service

6.1 Civil Defence 

6.2 Martial Arts 

6.3 Volunteerism 

7. Administration 

7.1 Academic Qualifications & Professional Affiliation 

7.2 Meeting records, letters, memos