A few weeks ago I wrote about the astonishing improvements that ChatGPT and other chatbots have made in solving difficult research data based questions, such as my quiz and exam questions. However, the ability of chatbots to critically analyse research data raises a much deeper question:

 

What should be the goal of my teaching in the age of AI? What is the purpose of teaching analytical and critical thinking skills and skills in solving scientific problems when AI can carry out these things for us.

 

With the onset of the internet age more than two decades ago, teaching students pure knowledge became increasingly obsolete. As a consequence, many teachers started to focus on helping students to improve their analytical and critical thinking, given that these were skills that students likely needed in their future. And so I, too, have over the past decade devoted much of my efforts in my teaching to introduce approaches to let students practice analysing, interpreting and predicting scientific data and solve problems.

 

However, the experience of testing whether ChatGPT can answer my test questions and solve the problems I discuss with my students during my classes made one thing very clear to me: There is no question that in the future most analytical and critical thinking will be done by AI.

 

Whether I like this or not and whether I think this is good or not, if people have data, they will ask AI to analyse them. What is more, AI will be built into any instruments and devices that generate data and will automatically provide all potential interpretations. People will also ask AI to explain things to them rather than coming up with their own explanations. And they will get AI to come up with solutions to problems, instead of coming up with their own solutions.

 

This raises two important questions. Should I still teach the skills that I have been focussing on in the past 10 years? And if not, then what should I teach instead, or at least in addition to what I am currently teaching?

 

To utilise AI, people will need some basic understanding of a given subject. In the case of my course this includes knowledge of how cells function on a molecular and organismal level as well as some basic knowledge of important experimental techniques. As such, one response would be to just teach the basics, assuming that students will not really need analytical and critical thinking skills.

 

This is of course what I have been refusing to do over my teaching career. The reason is that in the internet age what mattered was not what students knew, but how they could apply knowledge. But in the future this may no longer be true.

 

I could argue that by focussing on critical thinking about cell biology related problems, students also indirectly learn the basics. In other words, in my approach students learn the basics, i.e. fundamental principles and experimental methods, by applying it. This may be more engaging, but it may also be unnecessarily difficult (?) (which I explore further below).

 

On the other hand, given that University is meant to prepare students for their future, providing some basic foundational knowledge cannot be all that University education has to offer.

 

As such, are there arguments to still continue with teaching analytical and critical thinking and problem solving?

One potential argument is that in order to solve very difficult problems, students first need to learn how to solve easy problems. It is not possible to become a grand chess master by immediately playing against other grand masters. Playing against a chess grand master does not allow any chess novice to have any practice opportunities in order to improve. As such, it is necessary to start by playing opponents who are at a similar level and gradually build up the skills to play at a higher level.

 

Although this makes sense, what if AI can soon solve any problem. What if soon there will be systems with “artificial general intelligence” that can solve any problem better than any humans can. Some argue that this is almost guaranteed to happen. Indeed, AI can already beat any humans in chess. Of course, AI still often produces wrong answers, but what if algorithms are being developed that build powerful check mechanisms into AI systems.

However, there is one more important point. Despite the chess capabilities of AI systems, there are still millions of people who continue to be enthusiastic to play chess, in part because they enjoy the intellectual challenge. In the same way I still enjoy reading research papers from start to finish, look at data and try to make sense of them, instead of just feeding the paper into a chatbot and ask for a summary and for answers to specific questions I might have about the paper.

 

How much fun will life and work be if we enlist AI to solve all problems that we encounter. For example, how much fun will work be for a radiologist if all he or she does is to sign off AI generated diagnoses. In fact, what makes medicine exciting for many doctors is the challenge of figuring out what is wrong with a patient and finding ways to help the patient. In the same way, what is exciting for many researchers is to understand research data and come up with new hypotheses.

 

Are these reasons sufficient to justify continued teaching of skills that in the future can be carried our by AI tools? Perhaps yes, but there is no question that it is important to think about the tasks that AI will not be able carry out and the skills that AI is not able to deliver. Students’ future success is likely going to depend on mastering these skills.

 

Among those skills, there is firstly the skill to think creatively and ask good questions. And it could be argued that in order to come up with good questions, by for instance identifying assumptions that may be hidden in data, analytical and critical thinking skills are important. But more importantly, true creativity requires coming up with things that seem unlikely, daring and visionary.

 

This means that I should spend more time in my teaching on creative thinking exercises, letting students figure out what is unexpected in data and letting them come up with good research questions or truly unconventional approaches.

 

A second skill that Universities can and should focus on is to help students gain experience in solving real-world complex problems, preferably using AI tools. The success of future graduates will depend on their ability to solve problems in the current reality of widely available AI tools.

 

To create opportunities to practice these skills in my classes, I will have to learn new AI related skills myself. This is going to require time and effort, but there is no doubt that this is important to stay relevant as a teacher.

 

In this regard, one interesting assignment was described by university professor Ethan Mollick, whom I quoted in my post a couple of weeks ago and who gave students an assignments to make themselves redundant in a potential future job.

 

There are two additional skills that I believe will greatly matter in the future. The first is adaptability. In the same way that I have to adapt to a changing world as a teacher, students also need to learn to not shy away from difficult problems.

 

The other important factor that will determine future success are students’ personal attributes. While AI can come up with potential solutions to current problems, it is still up to us to turn these into reality. AI can generate many ideas, but if we cannot get ourselves to actually take consistent and effective actions, then AI will not be of great help to us.

 

As such, I feel that one my most important tasks is to help students to improve their personal attributes, such as their ability to motivate themselves, their ability for self-improvement and to overcome unhelpful habits, their eagerness to try out new things and their resilience.

 

To achieve these outcomes, a large class setting, as in my Cell Biology course, is not ideal. But it is also not prohibitive. As such, I feel that the personal individual conversations that I am conducting this semester with all students is a meaningful step to promote personal reflections, even though the conversations are not always going perfectly.

 

I believe that two of the most important personal characteristics that cannot be achieved through personal reflections are grit and persistence. Grit and persistence do not only determine our professional success and satisfaction, but literally every area of our lives, including our personal health and well-being. One of the best ways to build grit and persistence is in fact Marathon running, as this (in my opinion) amazing TED talk makes very clear …

 

This morning I went to work in a state of depression. I left home later than usual and got into the busiest rush hour, surrounded by people who by and large did not look enthusiastic to go to work, who seemed indifferent to their surroundings and who were distracting themselves with their mobile phones. I felt like one of them.

 

For the past few days, I have felt overwhelmed by work, not getting a break despite having school holiday this week. In particular, I have struggled with preparing my mid term test, reading numerous papers that raised ever more questions instead of answering any.

 

Then this morning, after getting into my office and starting to work, I finally figured it all out. And suddenly I felt this amazing sense of satisfaction and joy. When I went out for my lunch time exercise, I felt liberated, excited and truly happy. I still have a lot of work, but I am now looking forward to most of it.

 

The experience made me realise one important thing about my job. Although things are sometimes tough (mainly thanks to my own doing and taking up too many tasks and challenges), there are some truly exciting moments. These moments do not happen all the time, and they are hard to predict. But the possibility of experiencing true satisfaction and excitement is what makes my job so wonderful!

 

 

I have been quite consistent in spending some time daily to read books, as opposed to reading papers and articles online or listening to podcasts. And I have enjoyed this experience. It feels special and enjoyable to be sitting down in a comfortable position and environment and focussing only on the experience of reading.

 

One book I am reading is “Death – The end of self-improvement” by Joan Tollifson. The book focusses on the process of getting old, on the physical deterioration that inevitably comes with age and on how we can look at it differently. With her book, Joan Tollifson is helping me to view getting old as a normal stage of life about which there is nothing to be ashamed about.

 

One main reason why reading is so rewarding is that it often sends us off into directions into which we would normally not go and which are sometimes not even intended by the author. And so one particular quote from the book that got me thinking was something that the Jamaican spiritual teacher Mooji said:

 

 

A common pattern that is going through my professional and personal life is that if something goes well for a few times, I start to expect that it will always go well. If things then go against my expectations, I get disappointed or even upset.

 

This applies to how well my classes and quizzes or the student conversations I am holding this semester go. It also applies to whether I have peace and quiet while working in my office or while being at home, and even to whether things I order online (mostly records) arrive on time and in the expected condition.

 

Naturally, this mindset is bound to cause disappointment, depression and resentment.

 

The problem is unlikely that I do not appreciate the positive things in my life. In fact, I reflect almost daily about how lucky and fortunate I am to experience the life that I am living. Despite feeling a true sense of gratitude towards my daily experiences, I do become unhappy or upset when these experiences are taken away from me.

 

How can I overcome this mindset and live true to Mooji’s quote?

 

One approach could be could be to consciously consider the option that things might go wrong and plan for back-up options. For instance, instead of getting upset about the maintenance worker who disrupt my peace and quiet with the noisy (and rather useless) leaf blower in the morning, I could anticipate this disruption and arrange my morning routine differently. However, this approach is not very practical because it is impossible to prepare myself for every possible circumstance that might go wrong.

 

Alternatively, I could adopt a different mindset – to view things I do or experience as an opportunity. This requires that I do not hold expectations about what needs to happen or about what I need to achieve during certain activities.

 

A good analogy would be to not think that I have to fill my glass every day, but instead consider that my glass is already full and that all I am doing is to make what I have even better.

 

Importantly, adopting this mindset requires that I do not take up too many responsibilities, or else, my glass will always feel empty and I will experience the urge to fill it. This in fact seems to be the crux of the problem for me.

 

At the same time, life is ultimately about making a difference. As such, there is a fine line between maintaining my mental well-being by not taking up too many projects on the one hand and pursuing ideas and goals that I feel excited about on the other. And because external demands on my time change, there may be days or weeks where things get tough temporarily.

 

The most common reason why I experience feelings of stress is when unexpected demands suddenly crop up. Because I feel that these tasks take away time from doing my “important” work, I often dread or resent these tasks. As such, the best thing I can do is to consciously set time aside to deal with these tedious tasks. In fact, it is my experience that I can deal with tedious tasks much better if I have scheduled a time to work on them.

 

However, apart from not holding expectations, Mooji’s quote to “live as though one has no rights and no entitlements” seems to primarily imply that we should not assume any privileges and respect based on what we may have achieved. This requires a certain mindset. But for me it also means to live as though I have no rights and no entitlements by pursuing a simple live where I avoid luxuries and conveniences that make my life “easy”.

 

 

 

One year ago during my Cell Biology course I wrote about my experiences and thoughts on using ChatGPT in classes and assessments. I wrote about my strategy to let students use ChatGPT to come up with suggestions to test scientific hypotheses and to then evaluate the output of the chatbot. I also wrote about how ChatGPT does not pose any threat to my research data based assessments.

 

Fast forward one year and things have changed. ChatGPT has not only gotten better at testing hypotheses. It can also actually understand figures based on research data, which form a major part of my assessments. This means that students can now simply screenshot my research data based question, paste them into ChatGPT or other chatbots and get a coherent answer. The answers that ChatGPT comes up with are still not always correct. However, the reasoning is always sensible. More significantly, it feels that it is only a question of time until ChatGPT manages to answer all my questions correctly.

 

Apart from my research data based multiple choice or short free text answer questions, I also use another question format. Here students need to predict research data and actually draw the expected results. When pasting these questions into ChatGPT, it is currently able to predict the expected results rather well (although not always correctly, most likely because it has no access to what I have been teaching the students in my course). However, it is unable to produce any images.

 

In fact, when specifically asking it to “Can you create a Western blot image that shows the effect of myxothiazol and FCCP on hypoxia induced HIF-1alpha induction?“, it did produce an image which makes no sense and is rather amusing:

 

But again, it is likely only a matter of when, rather than if, chatbots will be able to solve these problems in the (very near) future.

 

In the past, I have felt reluctant to forbid students to use ChatGPT, even in my tests, for the simple reason that doing so means that my assessments no longer mimic real-life situations. However, this semester I finally decided to not allow students to use ChatGPT.

 

One reason is that this would not be a fair assessment. Moreover, it will do the students a disfavour. Getting a correct answer from ChatGPT without understand the answer will not help students in their future. Even if the students read and try to understand the explanations that ChatGPT provides, the process of coming up with one’s own solutions requires different skills from merely understanding someone else’s reasoning.

 

In the long run, however, I will probably have to think of new ways of assessing students in order for my tests and exams to remain authentic. The best approach is probably to test students in person, for instance by letting students them explain research problems, potentially even after they have consulted ChatGPT. Oral assessments are likely the best way to determine if students have really understood a problem.

 

Although oral exams help to avoid that we forbid students to do things in exams that they would do in real life, they may be difficult to implement in practical terms. However, more than thinking about the assessment format, it is important to consider what we should really assess students about in the current time. But I will leave this discussion for another post.

 

 

Recent news have revealed that large language models have become able to replicate themselves and resolve obstacles that they experience in this process. They have also been shown to mirror human personalities with high accuracies. Both of these news are scary, meaning for instance that generative AI tools can avoid being shut down and that they can be used for scams in unprecedented ways. As such, I spend more time to learn and think about the topic.

 

Over the past year, I have listened to various podcasts about generative AI and chatbots. As a result I have learned some interesting things.

 

To start off with a quote, Sam Altman, the C.E.O. of OpenAI, said this in an episode of the Ezra Klein Show podcast:

 

 

I find this quote remarkable for a number of reasons. It summarises in a very simplified manner how our brain functions. It also illustrates that the way large language models work resembles how our brain functions (at least to some extent!), where information flows through neuron-like nodes to generate an output.

 

The intelligence of humans compared to other mammals depends on having a big brain with many neurons and on feeding the brain information from which we learn to come up with solutions and creative ideas.

 

This explains why by increasing the computing power and amount of training data, large language models will continue to grow. In fact, as Dario Amodie, the co-founder and CEO of AI company Anthropic points out, this growth has been and is likely to continue to be exponential.

 

With regards to how large language models really work, they are in reality much more complicated than I will ever be able to understand (or potentially anyone).

 

Citing Brian Christian, an American bestselling author, programmer and researcher, who featured in another podcast of the Ezra Klein show, large language models are based on technology of deep neural networks consisting of nodes that similarly to neurons in the brain, receive inputs and release outputs. These nodes function as simple mathematical elements that receive inputs in the form of numbers. The numbers are added up and if the sum is greater than some threshold, an output (another number) is generated. Otherwise the output is zero. Having tens of millions of these elements, stacked into layers, the neural networks can accomplish complex tasks such as telling cats and dogs apart or distinguishing between cancerous and non-cancerous lesions.

One significant advance in the development of large language models was the creation of recurrent neural networks. This was achieved by adding recurrence or feedback information processes to neural networks. A good description of recurrent neural networks I found is that “Recurrent neural networks, also known as RNNs, are a class of neural networks that allow previous outputs to be used as inputs while having hidden states”. This is different from the function of the human brain, which only operates unidirectionally (information is transmitted from one neuron to the next and not backwards). Recurrent neural networks allow large language models to remember previous information, to recall what it has said before, to learn how different events relate in time and to carry out sequential tasks.

 

Nonetheless, recurrent neural networks still have a major shortcoming. They cannot remember long-term dependencies due to the so-called vanishing gradient. What this means is that as the temporal gap between the relevant information and the time point where the information is needed becomes very large, the information vanishes. To overcome this shortcoming, researchers developed Long Short-Term Memory Networks (LSTMs). These Long Short-Term Memory Networks incorporate complex feedback connections, which make the neural networks “highly effective in understanding and predicting patterns in sequential data like time series, text, and speech.“

An amazing post about LSTMs can be found here. What makes to post even more amazing is that it is 10 years old.

However, what is more relevant to me are the implications that AI has on my life and my work. One factor that makes this question especially relevant is the exponential growth of generative AI tools. Things that sound futuristic today may in fact become reality much sooner than we anticipate.

 

For instance, instead of just helping us to do things, AI may soon be able to do things on our behalf, like planning our entire holiday based on our own preferences. All we will have to do then is to show up on the day our vacation starts, board the grab outside our house and experience our dream holiday. Generative AI tools may even organise entire conferences autonomously. According to Dario Amodie the main bottleneck for this to become reality is unlikely to be the technology, which is expected to arrive within a short time of months to years rather than decades. Instead, the main hurdle is the readiness of humans to trust and adopt this technology.

 

With regards to the adoption of generative AI tools, a conversation that Ezra Klein had with Ethan Mollick, a professor at the Wharton School of the University of Pennsylvania, contained some good advice on how to use Large Language Models such as ChatGPT.

 

One core advice is that it takes significant practice (Ethan Mollick argues at least 10 hours) to become familiar with an AI chatbot, learn how to best use it and evaluate how it can help us. The best way to practice and test using generative AI tools is to use them for things we do in our work. We generally have some expertise in what we do in our work, which enables us to evaluate the AI output best.

 

Nonetheless, I must say that I feel a general reluctance to use AI for much of my work related tasks. The main reason is that it is much more fun to come up with ideas or solutions on my own compared to evaluating ideas or solutions that chatbots propose. Coming up with ideas and solutions feels like a fun thing to do. In contrast, evaluating ideas or solutions that chatbots came up with feels like work to me.

 

This parallels how I excited I feel about coming up with new ideas for my teaching and assessments and how I dread marking the tests and assignments that I have designed. Thinking of interesting problems to discuss in class or to test students on is truly fun. Having to then mark the tests and assignments is tedious and mentally draining.

 

What is also exciting to me is to bounce ideas back and forth with others and discuss solutions that others propose. The reason is probably that this process requires an active participation on my part.

 

On the other hand, I feel no motivation to engage in a discussion with ChatGPT about my own ideas or the ideas that ChatGPT produces. It may in part be because ChatGPT will probably agree with me and the discussion will go nowhere. I could of course ask ChatGPT to be in debate mode and question my answers, but then I also know that it only does so because I asked it to.

 

The more important reason is probably that there is something very special about talking to another human being.

 

Notably, one (perhaps not so) futuristic prospect that is being discussed is that people may soon prefer “AI friends” over real ones. I personally cannot imagine that this will indeed become reality. While I do not doubt that AI can be a much better friend (and by “better” I mean more understanding, “caring” and faithful), seeing a human reaction that validates or surprises us is something that I believe will always be important.

 

Finally, Ethan Mollick gave various suggestions to use ChatGPT for practical applications. One suggestion was to let AI read and evaluate what we have written while adopting different personas. I must say, though, that when I tried to follow this advice, I did not find the comments ChatGPT gave to articles I wrote particularly insightful. I also did not give good ideas on how to change what I had written, despite asking very specific questions (such as ‘Is the article interesting to read and what would make it more interesting to read?‘). What ChatGPT primarily did was to give general recommendations, without providing details on how to translate these recommendations concretely to my article.

 

In conclusion, generative AI tools perform unbelievably well in ways I could never have imagined. Yet, the ideas that really fascinate and inspire me still come almost exclusively from humans. What is more, engaging with thoughts and ideas of other humans and trying to come up with my own ideas and solutions seems so much more fun as compared to asking ChatGPT.

 

 

A recurrent theme in student feedback to my Cell biology course in this and previous semesters is that the course is very interesting and being taught well. However, the content is too difficult.

 

I suspect, however, that what students feel is difficult is not really the content itself, but applying the content to solves problems.

 

Contrary to the common student perception, I would argue that one main reason why the course is interesting is in fact because it is difficult.

 

Some courses are interesting because the lecturer discusses fascinating findings or phenomena and delivers them in an engaging manner. Although I try to include interesting discoveries and problems, I certainly do not deliver them by telling fascinating tales.

 

In contrast, what is different in this course compared to many other courses is that the learning involves solving challenging problems. Solving problems tends to be fun, especially when the problems have a certain degree of difficulty. For instance, most people would not choose to play a video game that has not some degree of challenge. If a game is not challenging, it tends to be boring.

 

Playing games with others is often even more fun. The same is probably true for solving problems in class, which is one reason why team-based learning is a great way to learn.

 

While it may be difficult to design a course that is at the same time interesting and easy (at least for me!), it is nonetheless useful to consider why students want the course to be easier.

 

Based on end of semester student feedback from past years, the main concern of students is not that they need to spend too much time on studying for the course and completing assignments. In fact, there are no major assignments.

 

Instead, the most likely reason why students want the course to be easier appears to be that the students are worried about their grades and that they may potentially end up with a bad grade that will pull down their GPA (and potentially ruin their future!). It is worth noting, however, that due to the grade moderation at the end of the semester the statistical chance for receiving a bad grade is not higher than in any other course.

 

As such, the main problem is not the likelihood of getting a bad grade, but the fact that students feel they cannot control their grades. If we take a course where we know how to do well and our grade hence only depends on our effort, we feel comfortable (although many students still do not actually do what they know it takes to get a good grade).

 

This raises the question of why do we want to feel in control of things? Being able to predict the future and stay away from uncertainty has advantages from an evolutionary standpoint. It helps humans to avoid dangerous situations.

 

However, it can also have disadvantages because we may miss out on things that are fun and interesting and help us to grow. Most importantly, all human progress is ultimately dependent on taking risks.

 

I would argue that the potential negative consequences of taking a risk in the context of choosing a study course is rather minimal. The worst that can happen is that we receive a worse grade than we had expected. I do not even remember what grades I got for the subjects I took in University, partly because nobody was ever really interested in them.

 

On the other hand, the useful outcomes of taking risks, even if we fail, are considerable. We may realise that our study method does not work in different contexts or that scientific research is not appealing to us. This is not even considering potential positive outcomes, such as gaining new perspectives or learning to overcome challenges.

 

Ultimately, doing well in my course requires the same approach as for most other courses, and this approach is practice. In some courses, doing well involves much practice to know the content well. In this course, doing well requires engaging in repeated practice to solve problems.

 

In fact, studies have shown that how well someone does in a problem based course is not dependent how “bright” a student is or how fast a student understands things. Instead, how well a student performs is a direct function of the skills that a student has at the beginning of a course and the number of practice opportunities he or she engages in. This means that a weaker performance at the beginning of a course can be completely compensated by more practice of the skills that are critical in the course.

 

Students often ask how they can practice more beyond the practice opportunities that are provided. My answer is usually that it is relatively easy for students to create their own practice opportunities. This may involve trying to explain problems to others (or even to ourselves). It could also be through using the same approaches that I often use to come up with questions, which is to vary experimental conditions or the parameters that are measured and predict the expected results.

 

While all this undoubtedly requires effort, I would argue that because solving problems is more interesting than merely revising content, it is easier to follow through with plans to study more consistently, which most students set out to do at the beginning of a semester.

 

It is the first week of a new semester, my first lecture is over, and I just watched the research talk that our departmental IT lead recorded last week in preparation for one of my next classes. And I must admit that I feel pretty depressed.

 

To be fair, not everything gives me reason for disappointment. The student activities, which took up the most part of my first class, went very well, and I also must admit that I explained the science in my research talk clearly.

 

But here comes the depressing part. After all these years, I still struggle to talk engagingly in front of an audience. The problem does not lie with what I am trying to express, but with how I do it. Over the years, I spent so much time to read, think and teach about giving engaging presentations and to help students with improving their presentations. But it did not seem to help me.

 

Of course, in order to be a good running coach, one does not have to be a fast runner. The same is true for helping students with their presentations. And hence even though I am not a great public speaker myself, I have been able to help my students quite effectively in giving good presentations, to the effect that none of my undergraduate research students has received a grade lower than A- for their presentation and project as long as I can remember. But it did not seem to make much of a difference for me.

 

The reason is of course quite obvious. Coaching others does not really help me in improving myself because by coaching others I do not focus on what are my weaknesses. In fact, I have never actively worked on improving my own presentation skills (except for trying to stop using excessive amounts of filler words), not to mention getting others to help me.

 

Truth be said, I have given presentations in the past that I was happy with and proud of. But if I had to say what I did differently in my good and my bad presentations, I would not be able to put my finger on it.

 

The good news is that it is never too late to take steps to improve ourselves. As such, I am going to add another goal to my list for the coming year, to finally work on my own presentation skills, in addition to trying to help others to present well. The first thing I’ll have to figure out is how to do it.

 

 

It is high time to think about my goals for this year. After spending much of my efforts over the past years on soft personal goals, I feel that I am ready and motivated to finally tackle some more concrete goals.

 

Specifically, there are three main things on my mind. Firstly, I would like to actually complete things, in other words, have something to show for all the things that I have been learning and improving. In fact, my original goal for this year was to accomplish one major achievement that I can add to my CV every month. Secondly, I want to increase the visibility of what I have been doing. Finally, I want to make progress towards my long term goals, coaching in athletics, health or pedagogy and making an impact in society, for instance through outreach.

Why are these things important to me?

 

With regards to achievements, adding things to my CV can give personal satisfaction. But ultimately the main purpose of achievements is that they benefit someone. What is the purpose of publishing a paper that does not provide others with actionable information or that nobody reads. What is the point of organising a conference that does not have some unique value for participants.

 

This brings me to the second point, why visibility is important. Visibility is important not to feel good about myself, but to let others benefit from my achievements. Likewise, the only way I am able to learn from other is because of the visibility of their output.

 

As such, I was considering this week what would be a good platform to learn from others and share my own insights and achievements. And I realised that one of the best options is LinkedIn. It is a platform that people visit to learn things. Hence, I would like to invest time this year to establish my presence in LinkedIn (although not right now (!), but after the end of this semester).

 

Finally, there are my long term goals. They are personal challenges that I want to master, but more importantly that provide meaning and opportunity for self-improvement in the future. Being long term goals, they do not necessarily determine my daily task list, but function more as a focus and direction into which I want to progress.

 

When considering goals, one aspect that one has to consider is the time factor, which to a large extent determines what I can achieve. The time factor is of particular significance for me because I do not want to pursue achieving things at the expense of doing my work well and spending time to help students. This highlights the importance of limiting my tasks.

 

However, there is a second reason why the time factor is so critical. It also affects how I experience the process of pursuing my goals.

 

There are various ways in which I do not want to experience pursuing my goals and doing my work. I do not want to be in a constant fight with my time to get all the things done I set out for myself. I do not want to feel pressured to finish all the work and worry that I may not. I do not want to feel I lost control over all the tasks that I have to do. I do not want to feel that I need to have managed to complete a long list of things in order to call the day a good day.

 

Instead, I want to do things because I enjoy them.

 

As much as we want to have control over the things that we have to do, for instance by using all kinds of productivity hacks, the truth is that it is impossible to gain complete control. Firstly, there is no end. There is no finish line. Many tasks take a long time, and we often cannot even predict how long they will take. Even after completing a task, there will be a new one. And many unpredictable things may happen on top of our task list.

 

I believe that there are only two things that are in our control. One is the amount of things that we try to accomplish in our day. The other is our mindset. Wouldn’t it be amazing if we could look at the things we have to do as things that we want to do? And if we do not get to do them, then it is fine!

 

While based on the nature of their work many people may not be able to do look at their work as a joy, I realised that I can. Instead of telling myself I need to read two papers, check the literature and write something every morning, I can say to myself that I want to do these things, and if I don’t, it is okay. And this is actually true.

 

Hence, what I have been trying for the last couple of days is to treat a new day as a holiday or a special day, even though it may be an ordinary work day. When I go into work on a holiday, it usually feels special, partly because of the feeling that I have the freedom to do whatever I want and everything I do is something “extra”, something I want to do. Why not adopt this mindset to every ordinary day.

 

But there are of course things that I have to do, for instance marking an assignment or report or preparing a quiz with a certain deadline. The good news is that I also enjoy these things for the most part (mainly because I get to decide how I want to design them). Nonetheless, I still feel the pressure to complete them on time.

 

When considering where this pressure is coming from, I realise that the main pressure comes from trying to achieve perfection. Hence, one shift I started to make is to not always expect perfection, but to accept imperfection. I will do my best, but if the outcome is not as good as it can possibly be, then that is okay.

 

What is more important than these small details is that my life is happy. After all, I am not responsible for creating a perfect experience for others. It is enough to provide opportunities for others to achieve a great experience for themselves, whether it is my students in the lab, my students in class or the runners in my running group. For instance, if my students do not want to submit a meaningful assignment, it is their choice and I do not need to try to provide meaningful feedback. What is more important is to continue to come up with new ideas and to strive for perfection where it is necessary, such as in assessments and things that I want to publish (not including this blog!).

 

Hence, in the new year I firstly want to focus on a limited set of tasks, so that the things I do become more enjoyable. Secondly, I want to focus on enjoying the process of creating and doing things, without worrying about the achievement of perfection.

 

How is this compatible with wanting achievements? I think that wanting achievements helps me to direct my focus into a certain direction. Hence, I still stick to the exciting goal that I set for myself initially, to complete one achievement every month this year, but I add to it that I want to accomplish it while experiencing joy and no pressure.