Taught by: Dr Charles Macdonald Burke
Name: V (Telegram Handle: @ver_nadium)
Content (Structure/Organization): -
Rather unstructured, going through various types of software in class and doing our own mini projects at our own time
Accessibility and Assessment: 4
Very accessible, at the start was more tailored to happiness but as the lesson went on, we were free to touch on other topics as long as it involves some extent of QR and utilization of data analysis software
Manageability of Workload: 5
It was okay, some weeks were more intense (since its mostly OTOT, intense periods for QR are when other mods did not have assignments)
Ease/Difficulty of Attaining Grades:
I'm not very sure myself...
Presence of Technical Learning:
Access to Datacamp premium accounts. Tableau, Excel, ArcGIS. And if you're willing to learn, Python, R, qGIS
Learning Value/Recommendation: 4
Gained confidence to write a paper with data analysis included. Can be quite useful for future data science portfolio work
About the Instructor:
You may ask him questions about the data and the topic at hand, he will give you good guiding questions that lead you to come to your own conclusions. But as for the actual data analysis itself, I think its more helpful to find out some things on your own as well
Additional Remarks:
Would be a bonus to go into this module with some basic coding skills!
Name: Allard
Content (Structure/Organization): -
Extremely flexible within and across classes. Within classes, discussions were often guided by students’ ideas and work, and across the afternoon and evening classes, different content might sometimes be covered.
Overall however, there were certain tools and concepts which Prof. planned to go through over the weeks, starting with the simpler ones. Initially, there was also quite a bit of focus on the topic of happiness, but ultimately there was no restriction placed on the topics students wished to explore on their own. Similarly for the data analysis tools students wished to use as well.
Accessibility and Assessment: 4
It was definitely not difficult for students with limited background to keep up with the concepts taught because Prof. Burke started from simple examples using Excel (e.g. opening up datasets and editing data), before moving on to other slightly more advanced tools such as the Survey Documentation and Analysis (SDA), World Values Survey (WVS) and Tableau. These other tools weren’t too challenging to pick up as well because Prof. demonstrated examples of using them in class at a pace where students could simply follow along if they were unsure how to start. Additionally, he also made it a point to offer his help during and after class to anyone who was having difficulty keeping up.
Concepts-wise, perhaps the more advanced topics were ones such as multi-variable regression and t-tests. While Prof. might not teach these the best he did share a number of articles, Youtube tutorials and Coursera videos which actually weren’t that bad. Moreover, since the whole module was based on Slack, students also had easy access to other students’ notes and materials which were shared.
Throughout the module, students were also given free access to DataCamp and Tableau, as well as a list of links to resources and courses. So as a whole, the support provided by Prof. was not lacking, but a significant level of independence and self-directed learning was required on the students’ part to really take advantage of it. That could be a challenge for students with little to no background in working with data, or who might be lacking the confidence to try things out. In fact, the unstructuredness of this module might be detrimental given the many possibilities and lack of direction students might face.
Manageability of Workload: 5
Extremely light with no compulsory assignments or exams, besides occasional reminders to post updates on Slack. In-class examples were manageable too. With the freedom allowed to plan our work schedule, the module was also a good balance to other more rigid and stressful modules.
Ease/Difficulty of Attaining Grades:
Definitely achievable as Prof. seems generally more liberal in giving decent grades compared to others. While there is no clear metric for how one is awarded an A besides the “4 Pillars” (Data, Software, Topic, Analysis), it might be good to at least post on Slack occasionally, showing evidence of thought in analysis and not just posting for the sake of it. Other possible positive qualities to demonstrate include (non-exhaustive):
- Thoughtful replies to peers and Prof. (following up on Prof’s questions/comments)
- Working with peers on projects (something he suggested from time to time)
- Being active in other channels as well (ask questions, provide help, share resources, etc.)
- Not being afraid to ask questions when in doubt
- Sharing more on the thought process behind the work besides what was done or achieved
- Demonstrating some form of class participation (doesn’t necessarily mean speaking up in class, it could simply be following along with the examples shown in class and sharing some thoughts on them in a Slack post)
Another thing Prof mentioned was that he also collects data on us students and would use it to give him a clearer picture of our progress in the module. This likely means data collection through DataCamp (points attained and courses completed) and Slack (number of posts made, number of users posting in your channel, etc.). Though it is not simply a matter of quantity, it might be helpful to keep this in mind.
Presence of Technical Learning:
Technical skills include Excel (V-lookup, merging datasets, graphs, etc.), Tableau (joining datasets, creating relations between fields, dashboards), T-tests, regression, multi-variable regression. Other analysis platforms include the World Values Survey (WVS) and Survey Documentation and Analysis (SDA). This list is non-exhaustive.
Learning Value/Recommendation: 4
Overall somewhat useful, because of the free access to resources such as DataCamp which one can earn certificates from, and Tableau which is a popular data visualisation tool. Besides that, most of the technical learning outcomes can actually be achieved in one’s free time without taking the module.
Beyond the technical aspect however, Prof. shared some useful ideas on topics such as competition, tacit learning, inductive and deductive reasoning; ideas which could bring significant benefits even outside this module. His perspectives on the future of work and on teaching and learning were also rather insightful. Additionally, Prof. was open to consultations and it was quite helpful seeking his advice and feedback on the work done, especially given his background and experiences (take advantage of them!).
In short, the learning value ultimately depends largely on the student.
About the Instructor:
Prof. was very approachable, and extremely supportive and encouraging in his feedback, though a similarly extreme unstructuredness in the module might not suit everyone’s learning styles. While Prof. may not be the best teacher of concepts, he can be rather resourceful in pointing students in the right direction.
Name: Cheryl Doo (Telegram Handle: @Cheryl_2o13)
Content (Structure/Organization): -
The module is centered around 4 pillars or goals - topic (how much you know about happiness), data (how to collect and make sense of data), analysis (how to run statistical analyses) and software (you ability to use different software in QR). This is the basic (and kind of the only) structure this module has. The rest is pretty free flow. Prof uses slack to monitor everyone's progress in QR in the form of individual projects. Prof's seminar lessons are dependent on our needs as he evaluates and comments on our individual projects, and he has no strict goals for how our projects progress. This is dependent on you (what topic you want to explore, what skills you want to learn etc.) which I think i grew to like because of the freedom of exploration and OTOT-style of work. The no structure thing was new to me, and I did feel skeptical about it and how i would fare in it, but this independent learning, and learning from prof's feedback and other people's channels are quite enriching.
Accessibility and Assessment: 3
Fairly accessible. The module is formatted such that the prof looks at each individual's unique progress so it doesn't matter how much you know at the beginning of the course, as long as you make steady efforts to improve your QR knowledge, you're good to go. However, not knowing anything will mean that one has to be proactive in searching for information and learning independently, otherwise it will be difficult to progress, so this can be a challenge especially when one's peers may be at a knowledge level more advanced than you.
Manageability of Workload: 4
This module is quite "free and easy" in terms of workload. It depends on how deep you go into your individual project and what you want to get out of it. Every week, people usually posted on their slack channels once or twice. This posting occurs outside of seminar lesson time. And during busy exam seasons, prof also gives you the option of not going for class if you don't need to consult him on your project so that frees up your time to focus on your other assignments too.
Ease/Difficulty of Attaining Grades:
Fairly achievable. The prof is quite objective and not very harsh in grading (he believes that as long as you have progressed consistently from the level you were at when you started the module, you deserve a good grade. He also said that if he could, he would give everyone an A) but of course, there's the bell curve. I would say therefore that it is achievable to get a good grade, but to get an A specifically (which is the highest score) can be difficult bcos it is dependent on how much your peers have progressed and their quality of posts as well, not just your own.
Presence of Technical Learning:
Prof goes through some basic skills (eg. What linear regression is) and teaches us how to use some software (eg. Tableau, arcGIS). He also gives us access to datacamp, where coding and other tech-related lessons are free but how much you learn is also dependent on how much time you spend learning new things on your own, inspired by your needs as you are doing your project.
Learning Value/Recommendation: 4
I think i have learnt alot from this module. I have become more proactive in seeking out information, improved on software skills and QR abilities. More importantly, i have learnt some life perspectives which are valuable moving forward in life, and this is something i enjoy in his class, that prof always seeks to establish purpose for what we are doing instead of just teaching the technical skills. For it is purpose and perspectives that truly enrich you as a person.
About the Instructor:
The prof is quite knowledgeable in this domain, given his deep interest in QR and playing with data. He is good at guiding you in your thought process of how you should ask questions about data and the ways to go about solving them. He aso knows a variety of software and analysis tools. He also communicates concepts well such that it is simple to understand. He encourages questions in class so even if you don't understand the concept, he is open to helping you understand so that everyone can progress as a class. One thing to know is that he tends to talk alot (and sometimes ramble) during class, so it's important to pay attention to the important parts and not drift off.
Additional Remarks:
Prof is friendly and enjoys discussion in class, so if you have an idea/comment, feel free to express it vocally!
Name: Shane Lim Yu Xian (Telegram Handle: @asparaguschan)
Content (Structure/Organization): -
The module is largely an unstructured one as one of the key emphasis of the module is to encourage self-directed learning. However, despite its lack of structure, the onus is on one to self-impose a mental framework to help navigate the module whilst developing an effective self-directed learning pedagogy (e.g. narrowing down to the intricacies from the bigger picture). It is thus a good opportunity to kill two birds with a stone, to not only learn about quantitative reasoning but to also be more acclimatised to self-directed learning which will prove useful in on-the-job training in the future workplace environment, especially in light of the emerging remote working culture.
Accessibility and Assessment: 5
Given that the topic is largely on quantifying one's state of mind (i.e. Happiness), the underlying concept of the module is highly intuitive with little to no help required to comprehend the module. However, the use of software and data analytics tool might come as a challenge to those unfamiliar with data analysis (notably Python, Tableau and ArcGis). Nevertheless, Dr Charles will take the time to explain the use of such tools, hence rendering the learning curve of the module to be relatively less steep than expected.
Manageability of Workload: 3
The workload can be heavy should one desire it to be, and light should one desire it to be. I personally viewed the module as a learning journey as opposed to an academic regime, that I sought to give myself pockets of time each day to reflect on the topic and should I be hit with a 'Eureka' moment, I will pen down my thoughts and use the appropriate tool to generate insights and share them with my fellow peers and Dr Charles on Slack, the main platform for communication. While some share more insights than others and some less than others, the key is to prioritise quality over quantity, which can be done by simply asking oneself: Do my insights truly interest me?
Ease/Difficulty of Attaining Grades:
Such a question can only be answered through one's desire to commit to the module. Given that there are no graded assignments or test, it is important to remain consistently engaged throughout the module. I personally perceive Dr Charles as a fair grader that is neither liberal nor harsh, and grades in accordance to one's ability to practice Quantitative Reasoning in a critical and independent manner.
Presence of Technical Learning:
The technical skills that one learns from the module is dependent on one's personal learning objective. For instance, should one seek to perform forecasting or Hypothesis Testing, one will be able to hone one's Excel skills using Data Analysis tools within the said software. On the other hand, should one seek to improve data visualisation, one will then be able to hone one's Tableau skills (on a more specific note, should one seek to better understand the more niche data visualisation skill of spatial maps, one can seek to hone one's ArcGis skills). Should one seek to achieve a higher level of data analysis with voluminous amounts of data, one will then be able to hone one's Python or R skills. The bottom line (and the beauty of the module) is that Dr Charles provides one the liberty to use a wide, non-exhaustive range of analytical tools throughout the course of the module, thus allowing one to better tailor one's learning needs and wants to that of their current and desired ability.
Learning Value/Recommendation: 5
Personally, the key takeaway from the module will be the human choice between being a 'satisfier' and a 'satisficer'. While each of us might have a different takeaway from the module, it is of essence that we utilise the key skills of Quantitative Reasoning, inclusive of inductive and deductive reasoning, to validate and support our beliefs of what entails the 'key' to happiness (or the fact that happiness is a process as opposed to an endpoint).
About the Instructor:
Given Dr Charles' experience as a geographer, he does possess the skills to understand human behaviour through the analysis of data. As one who prefers to ensure that students are able to comprehend concepts as opposed to intensively cramming content into lessons, the absorption of content is optimised as the right amount of content is covered in the right amount of time through effective communication.
Content (Structure/Organization): -
There really isn’t much structure or organisation to Dr Charles’s classes. Prof decides the content of his class based on the progress and needs of the class, ensuring that we are able to benefit from his classes.
For the first few classes, we were reading articles during class time (which prof would ever spend 30min of class time asking the class to read an article) and then we would have discussions based on the content we just read. The next few sessions we worked through the World Happiness Report together, doing data analysis and drawing conclusions from the data we worked with. Dr Charles received some feedback that he should teach us some software skills so he did in the next few weeks. We touched little on excel and Tableau where Dr Charles would lead the class in cleaning, analysing and visualising data using these softwares. In the second half of the sem, Dr Charles wanted us to focus on something more localised so we did not focus too much on the topic of happiness, instead we tried to draw conclusions from local data that would be able to make a positive impact in our society.
Dr Charles truly believes in self-directed learning so he gives us a lot of freedom in deciding what we want to learn, topics that interest us, mini projects that we want to conduct, and he provides assistance and feedback along the way. Hence, this mod is really very fluid and there’s little to no structure. If you are someone who needs clear direction and structure, you may not enjoy this module.
Accessibility and Assessment: 4
Dr Charles able to cater to the needs of all his students. In the class I was in, some people has absolutely no background knowledge on QR or stats while others were able to use coding software to clean up and analyse datasets. Dr Charles is very personal when it comes to providing feedback which ensures maximum learning for his students, the advanced ones can continue with new discoveries while the weaker ones are not left behind
You absolutely don’t need to have any background knowledge in QR/stats/data analysis to be able to understand/cope/excel in Dr Charles’s class! Dr Charles does not believe in teaching us a set of hard skills and then letting us apply it to our learning (which is typically how classes are taught). Instead, he gives us the liberty to do anything we want, literally anything. And in the process when we realise we need certain skill sets or knowledge for us to interpret/analyse/present our data and findings, Dr Charles then encourages us to be independent learners to google and try to self-learn that particular skill. He steps in we have difficulties learning that skill or understanding certain concepts and then he will do his best to explain it to us.
Dr Charles isn’t too concerned about the hard skills we pick up through QR per se, he is more concerned about how we think and look at problems- the questions we ask, the approach we take to resolve these issues.
Manageability of Workload: 5
As bizarre as it sounds, we did not have a single assignment throughout the semester. Dr Charles uses the platform Slack for his module and he grades us through it. We are encouraged to upload interesting articles relating to the topic of happiness or even content that interest us and discuss about it. Whenever we conduct data analysis we will also upload our findings on the Slack page for Dr Charles and our other classmates to comment and add value to it. If we decide to watch some online tutorials to pick up some new skill, we are encouraged to also include it in our Slack page so that Dr Charles is aware of our progress.
There are no assignments at all so our grades are given to us based on the amount of growth and improvement we made since the first day. Hence, the Slack page is crucial in reflecting our learning outcomes and the progress we made. Although there are no official assignments, Dr Charles encourages us to collaborate with each other on mini projects, working on topics that interest us. All these will be taken into consideration when he grades us.
As a result, workload is really very chill. It really depends on how much time and effort we want to devote into this module. Dr Charles never demands us to produce any work, in fact, he is always very understanding and keeps reminding us to slow down and take breaks (because apparently we were all very active in updating our Slack pages). During mid-terms period, Dr Charles wouldn’t expect us to post on Slack, the most he ever asked for was to read any article and share it in Slack (truly quite slack).
Ease/Difficulty of Attaining Grades:
Since Dr Charles is a prof who values the process > outcome, he is very liberal in giving out good grades. As long as he sees you putting in the effort and sees an improvement, that’s considered a win. Unfortunately, prof can’t award the entire class with an A grade (he would if he could), so some students would get A-/B+, but I doubt anyone has gotten lower than a B+ grade.
The only thing is that his grading is based on a long term growth so its tough to gauge ‘how well you will do’ since there isn’t any CAs that can indicate the sort of grade you may attain. In Dr Charles class, just do your best and be self-directed in learning, scoring well (B+ and above) shouldn’t be an issue.
Presence of Technical Learning:
The technical skills that one learns from the module is dependent on one's personal learning objective. For instance, should one seek to perform forecasting or Hypothesis Testing, one will be able to hone one's Excel skills using Data Analysis tools within the said software. On the other hand, should one seek to improve data visualisation, one will then be able to hone one's Tableau skills (on a more specific note, should one seek to better understand the more niche data visualisation skill of spatial maps, one can seek to hone one's ArcGis skills). Should one seek to achieve a higher level of data analysis with voluminous amounts of data, one will then be able to hone one's Python or R skills. The bottom line (and the beauty of the module) is that Dr Charles provides one the liberty to use a wide, non-exhaustive range of analytical tools throughout the course of the module, thus allowing one to better tailor one's learning needs and wants to that of their current and desired ability.
Learning Value/Recommendation: 4
I would say that the thought processes would be my largest takeaway: the way I look at problems around me, the kinds of question I would ask and my approach in tackling it- this was definitely an area that this QR mod exposed and trained me in.
About the Instructor:
Dr Charles enjoying working through the content and datasets with us as a class and making discovering/forming conclusions along the way. Although he may not be teaching us a new skill or content upfront, prof knows how to push us and challenge us in providing multiple perspectives and questions. If you are looking for a know-it-all prof then Dr Charles may not be the one, but he definitely provides fresh perspectives that will add a new dimension to the work we present to him.
As a person, Dr Charles is very approachable and chill. We have a random channel in the Slack platform where we post random stuff (memes, silly videos, jokes) as a class and prof really enjoys it. He will comment and laugh about it, sometimes he himself will contribute and post random things there too! We edited Dr Charles into a meme and he loved it! When it comes to work, prof is very willing to have individual consults with us to help us understand concepts or work through our datasets with us. He also makes it a point to comment on all of our Slack post and provide feedback. Dr Charles creates a safe classroom environment where he takes all questions, even silly ones. He always encourages us and ensures no one gets left behind. I honestly think Dr Charles is a great prof, perhaps his style of teaching isn’t for all, but if you are able to deal with self-directed learning with little to no structure, I would highly recommend taking a mod under Dr Charles, truly a very student-centered and caring prof.
Additional Remarks:
I encourage those who are considering to take Dr Charles’s mod or this QR mod to really have an open mind! Although Dr Charles and his mods are not the most popular, it definitely has its learning value. Ultimately in Dr Charles’s classes, its all about how much you are willing/want to learn and the effort you are willing to devote. If you enter with the right attitude, it will be a fantastic and fulfilling learning experience!