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On the evening of 3 Feb 2021 I attended a briefing for a new module, Machine learning and artificial intelligence (TM358), that is scheduled to begin in October 2021. 

This short blog post represents a short summary of what was covered during the briefing. I must, however, begin with a short disclaimer: some of the detail that is presented here may well be subject to change as the module moves to production.

The briefing began with Neil Smith, module team chair, who said that “machine learning has been one of the biggest changes in computing in the past 20 years”. Neil also said that artificial intelligence has not featured within the computing curriculum for 5 years. In some ways, this module fills an important gap in the computing undergraduate curriculum.

TM358 is a part of a new qualification: BSc (Hons) Data Science. R38, as it is known, is a joint degree with the schools of Maths and Stats and Computing. To study the module, there are two important prerequisite modules: MU123 Discovering Mathematics and M269 Algorithms, data structures and computability.

From the computing side of the pathway, students can also study TM351 Data management and analysis.and TM356 Interaction design and the user experience. Students would begin their level 1 computing studies by studying TM111 Introduction to computing and information technology 1 and TM112 Introduction to computing and information technology 2.

TM358 has a particular focus on deep neural learning. I made a note that the module adopts an engineering approach and makes use of toolkits and languages that may already be familiar to some students. There is also strong thread of social impact and the importance of ethics. Key tools that students will use include the Python (featured in TM112) and Jupyter notebooks (featured in TM351). Datasets that students will be using will be provided by the module team.

Like many modules, it begins with an introductory (or foundation) section, and then subjects are introduced through a series of study blocks. 

Foundations

This first section sets the scene and also presents a historical perspective. It also introduces what is called the “compute environment”, which is the environment that students will be using and studying. This first section will introduce different types of data, mention datasets, and introduce concepts and terms which will be later explored. 

Block 1: Introduction to neural networks/deep learning

This first main block introduces artificial neural networks and some accompanying mathematics. The module offers students a handwriting recognition example. It also looks at AI and machine learning transparency challenges and what they may broadly mean to society.

Block 2: Image recognition with conventional neural networks

This block looks at limitations of traditional neural network systems. It examines the challenge of image classification. Students will be introduced to the concepts of neural network training, and data bias issues. 

Block 3: Recurrent neural networks and long short term memory networks

Some key questions that are asked by this module includes: why do we need sequential modelling, what are the differences from the previous types of learning? Applications such as a speech recognition and sentiment analysis (which is about looking at whether things are views positively or negatively) are used. Recurrent neural networks (RNNs), bidirectional RNNs, long short term memory networks (LSTM) are studied. 

Block 4: Unsupervised learning and autoencoders

I noted down a question that is addressed in this block, which is: what is unsupervised learning? Another topic autoencoders and their structure. I also made a note that there is a section about ethical issues.

Block 5: Alternatives to deep learning

Although there appears to be an emphasis on neural networks, it isn’t the only approach. This block says something about other approaches, such as, decision trees and Bayesian methods, exploring the reasons why different approaches might be chosen. Students will be using notebooks to study different datasets.

Block 6: Handling data

There is another question to answer, which is: why do we need to pre-process the data? I noted down the concept of discretisation and discretisation techniques. Another question that is addressed is: what is the effect of imbalanced data on learning algorithm performance? The block also covers solutions for the classification of imbalanced data.

Tuition and assessment model

Tutors will be required to give 10 hours of tutor or progression time. Progression time refers to time that isn’t allocated to tutorials but is used to help with student support and guidance. All tutorials will be delivered online through 2 clusters (groups of tutors). There is expected to be a tutorial to help students to prepare for each TMA and the EMA, with another tutorial for each block.

The module will use something called single component assessment, which means that the TMA results directly contribute to the final score, as opposed to students having to get distinctions in both the TMAs and an examinable component to gain a distinction.

There will be 3 TMAs (with an increasing percentage to the overall score), with an EMA contributing to 60% of the final score. For the EMA, “students be given a dataset and a task to accomplish using the techniques and tools taught in the module”.  Students will also “write a report detailing the actions taken, justifications for the actions and decisions taken, results achieved, their understanding of the results and any ethical issues.”

Reflections

I studied AI as an undergraduate student, and again as a postgrad. My undergraduate AI module contained a lot about how to solve problems by searching (we also used a fancy language called Prolog). My postgraduate studies touched on the interesting philosophical questions that thinking about intelligence immediately provokes. I also remember that the last AI module that the OU used to have, M366 (if I remember the module code correctly) had a slightly different character to it.

There were terms in the TM358 that I didn’t recognise, which suggests that things have certainly moved on a lot since I have last studied AI. Two substantial changes may include the substantial increase in processing power that we now have at our disposal, and the availability of tools that we can draw upon to analyse data.

In terms of this module, it’s practical focus clearly comes through from the briefing. It seems to be about doing stuff, understanding tools and, significantly, understanding some of the ethical issues accompany the use of these tools.

Since I have enough on as a tutor (I’m tutoring on a second level module, and a project module), I don’t have any capacity to even consider making an application. This said, I do encourage other to consider making an application, since it does look fun, and challenging too. It strikes me that there is certainly a lot to learn. 

Acknowledgements

With all tutor briefings, thanks go to all members of the module team, led by Neil Smith, who all gave presentations during this short briefing session. Some of the notes presented within this blog are drawn from a PowerPoint presentation that was made during the recruitment briefing. Acknowledgement are also given to curriculum manager, Sarah Bohn and Staff Tutors Christine Gardner and Frances Chetwynd. 

Earlier this year I wrote a short post to summarise a TM112 tutor briefing that took place close to the Open University headquarters in Milton Keynes in March 2018. The aim of that event was to introduce the module to tutors, enable them to meet each other, and form them to ask questions.

Since TM112 Introduction to Computing and IT 2 (OU website) starts twice a year (once in April and again in October), this blog post is a summary of the second TM112 briefing.

In many respects, this briefing was really similar to the first: members of the module team introduced the different blocks of the module, I spoke about some of the ideas behind the group tuition strategy, and we looked at a marking exercise to get a feel for what kind of teaching we would be doing. 

There were three parts of the briefing that were (to me) particularly memorable. 

Python programming

The first part was a talk by Richard Walker, who is an associate lecturer and member of the module team. Richard spoke about ‘Problem Solving with Python: approaches and projects’. A point I noted down was that a common issue in the teaching of programming is a lack of emphasis on the importance of problem solving skills. Also, there is a misapprehension that programming can and should be fun, since it is an inherently creative activity. Also, importantly, students can have misleading mental models of what happens within a language. Whilst learning programming can be difficult, it is important to nurture what is known as a growth mindset; that it is possible to get better and develop through practice.

Computer Security and Privacy

The second part was presented by Mike Richards, who also gives what is called the ‘guest lecture’ on TM112. Mike introduced theme 3: information technology in the wild. He spoke about CIA: confidentiality, integrity and availability, recommended that students created what was called a diary of reading (to collect news stories about cybersecurity). He also said that the module introduces encryption, mentions the dark web and blockchain before mentioning a case study of a high profile cyber attack. He concluded by touching on wider (and important) issues of freedom of speech and the way that algorithms can potentially influence our lives and civic debates.

Tutorial planning exercise

During the briefing, we were divided up into groups, and asked to create a hypothetical plan for a tutorial that was connected to a module topic. Our group comprised of myself and two other tutors. We were given the topic of ‘location based computing’. What follows is a rough tutorial plan. If you randomly find this blog post, do feel free to borrow, modify and steal this plan!

1. Use a poll to ask everyone their views about location based computing. Are students: happy, unhappy, worried, or don’t know.
   
2. Begin a discussion to ask everyone if they have any examples of location based computing, and also to get an appreciation of what everyone understands by that term.
   
3. Sharing of examples: one example that was discussed was a technology to keep track about where your child or partner is. Whilst this can help with safety, it also has privacy implications too; every technology can be used for good and bad things. Another example are the alerts on your mobile phone which appear after visiting places. Are there issues about using of social media? What is exposed when you tweet or update Facebook? There are some positive examples too, such as sharing maps of areas where you have gone running.
   
4. One interesting idea is to demonstrate location based computing using some Python code. Tutors might demonstrate how pins can be added to Google maps, or there could be a service to show how far everyone is from the university head office in Milton Keynes. This could be done by screen sharing from a tutor’s computer.
   
5. After a final closing discussion or a summary, the tutor could present everyone with a second (anonymous) poll to see if anyone has changed (or developed) their opinions.
   

Reflections

I always like tutor briefings, and I especially liked the tutorial planning activity; I can’t remember ever having been a part of this before. I also really liked the ideas that we came up with. A personal confession is that I’ve not used polls within my own online tuition practice, and that is something that I feel that I need to figure out how to do. I also need to learn how to get a more thorough understanding of how to use screen sharing too.

During my part of the briefing I said, ‘by the end of this module, tutors will be teaching in innovative ways and doing things that the module team had never dreamt of’. I firmly believe this.

Acknowledgements

Many thanks to the two fellow tutors who contributed to the discussions about the above tutorial plan. You know who you are!