E-Learning, Digital Learning, Digitally Transformed Learning---Do They All Mean the Same?

Nowadays we often hear that the term "e-learning" is a bit outdated, we should rather speak about "digital learning" or even "digitally transformed learning" when we talk about things like using learning management systems (LMS), students collaborating online, etc. Are these terms interchangeable, then? Does it even mean that in learning we are ahead of other areas which are yet to be digitally transformed---right now or in the near future---as we have been doing e-learning for 15 or 20 years now, or perhaps even since the 1960s (have look at PLATO)?

As for other fields, you could talk about the aspects of digitization, digitalization, and digital transformation of learning, and what they imply when it comes to skills and competences you should have and you could acquire. We will do this in a separate article.

In an attempt to look at similarities with other areas, we could try to define various waves or eras of learning, or even try to define "learning revolutions" in analogy to "industrial revolutions." And then we would arrive at terms like "Learning 4.0" (to have the same version number as in "Industry 4.0," or rather "Learning 3.0," or maybe "Learning 3.11 for Workgroups---OK, just kidding). And then we would need cover terms to name the eras of learning.

When we take the industrial revolutions as tertium comparationis, we have the first revolution with the advent of looms---mechanical work done by hand had been automated on a small scale---and the steam engines---the automation of mechanical work at a larger scale. The second revolution was the advent of electricity. This also involved the introduction of the production of electricity as a utility, as a service. It became possible to produce energy at some place and transfer it over fairly long distances to run machines. It wasn't necessary any longer to produce energy directly in or very close to factories. Ford introduced assembly lines and mechanical work done by hand changed again, workers specialized in specific areas. The third revolution came with the introduction of computers, we got CAD/CAM, industrial robots, etc. In all those processes, the human was the main factor: humans control and regulate, they make decisions with the help of machines.

Now in the fourth revolution we face the merging of real and virtual worlds, we not only interact with machines, we let machines decide and call this digital transformation. Computer programs decide whether or not you are creditworthy---some years ago, the banker would inspect the gathered and aggregated data and then make a decision; now the computer decides on its own based on models it created from relevant and irrelevant data using machine learning. We are close to let machines decide whether or not you are prone to return to your bad habits after rehab or prison (see, e.g., AI Judges and Juries in the December issue of CACM).

Let's look at education, where can we position "e-learning"? With the advent of tele-learning in the 1950s and 1960s, we find some aspects of automating parts of teaching. But even earlier, in the 1920s, we have actual machines: mechanical devices as first introduced by Sidney Pressey to let people answer multiple choice questions. Later Skinner developed them further to provide automatic, immediate, and regular reinforcement, and thus trigger learning. And it could be shown that students actually learned while using those machines. Already then we find the discussion whether or not machines would replace human teachers in the near future.

When we look at developments in the 1970s, with the PLATO systems, we find the same ideas: to provide automatic, immediate, and consistent feedback. That's part of "teaching," though, it doesn't redefine "learning"!

Then, at the start of the 21st century, we integrated computers into teaching and learning. We often talk about "e-learning," but we only rarely talk about "e-teaching." However, even with LMS and all of their still improving (or let's rather say: accumulating) functionalities, we still focus on automatic, immediate, and consistent feedback. That's what all the e-assessment, peer activities, forums, etc. are about. And we all agree that just using your fully-fledged LMS to distribute your PowerPoint slides doesn't qualify to be named "e-learning." But still, we have no actual interaction of human and machine, you just get feedback and then decide what to do next. So "e-learning" in this sense is just a contemporary (as in "use mobile electronic devices") teaching machine.

But wait, we also had intelligent (adaptive) tutoring systems in these first years of e-learning! Actually, those teaching machines by Skinner as well as the PLATO V were also intelligent tutoring systems (ITS)---and they were advertised as such. So also this isn't a brand new idea! For various reasons, these systems weren't successful at the time. But most contemporary e-learning research doesn't refer to those old publications when talking about adaptive systems. But maybe now, given the available computing power, it would be time to revisit those old ideas. If technology (including bandwidth) for distribution and interaction was the bottleneck back then, we may be able to solve those issues now.

There have been various attempts around the start of this century, though, tackling another potential bottleneck: the learner model. Using computers, it was more comfortable to implement and maintain various learner models accounting for different learning paths through the material towards the final goal of acquiring some specific competencies or skills. Attempts like <ML>³, (Multidimensional Learning Objects and Modular Lectures Markup Language) or elml (eLesson Markup Language) aimed at foreseeing learning paths and provide students with the appropriate next steps depending on previous actions and (formative) testing outcomes. It turned out that creating such material was rather challenging and demanding. The same was true for testing formats like SET (Satzergänzungstests), which allow you to answer a question by adding parts of sentence(s), an instantiation of "Reihenerweiterungswahl" (Closed Sequence Selected Extension Items) according to the typology of Rütter (Rütter, T., 1973. Formen der Testaufgabe. Eine Einführung für didaktische Zwecke. C. H. Beck, München.) as we showed in a paper. Even with a rather sophisticated editor, it was a nightmare to produce those tests.

Writing learner models using rules, manually, is probably not working. One simply cannot foresee all possible activities and interests. A truly adaptive intelligent system would need a model covering all those possibilities. For now this seems achievable only by using machine learning. In the way we construct language models we could create learner models to feed into tutoring systems and let the machine decide what experience the learner makes next, what problems the learner should solve next, etc. And there we would have it: the digitally transformed teaching/learning as a blending of real and virtual worlds with the machine not only providing information to support human decision-making but with the machine deciding and interacting with the human. Of course this also raises ethical questions: is it OK to have the computer model you as a learner? But that's along the same lines as in "is it OK to have the computer model you to decide whether or not you will get this credit or whether or not you can get that life insurance?"

As long as we identify "e-learning" with "using the full potential of LMS and (apps on) mobile devices" (does anybody remember "clickers"? You can have them as apps now, yeah!), we don't talk about the digital transformation, but about the electronic re-engineering of teaching machines. But as long as we're just deploying "electronic teaching machines," we should stick with the term e-learning. Oh, and we still have vast communities who use LMS as PDF or PowerPoint distribution vehicles only, there isn't even digitalization involved, only digitization.

Clearly, with all those MOOCs around where you interact with the video and the instructors/tutors, a lot of logging is could be going on. This data will be used to model learners. And as for language models in Natural Language Processing, those models created by machine learning might be not exact but appropriate or good enough for specific tasks. The big issues there revolve around the questions of "which features matter, which features do you use?" The same will be true for learner models or learning models. What we have in e-learning are various models of teaching, and those could be described by manually crafted rules. They are based on hundreds of years of research and developments in didactics and pedagogy (and schools thereof). For determining and weighting features for learning, we shouldn't leave the fields to the usual suspects of Big Data processing. This research and development and thus the digital transformation of learning should be driven by the field of teaching and learning, by the experts involved with didactics and pedagogy.

Supervision families

When you do your PhD, you‘re not completely on your own, you have a supervisor. And then maybe a PhD committee and a second and a third reader and so on. However, during the process of developing your research question, diving into literature and possible approaches, making discoveries, and finding a place for your future academic persona, you primarily interact with your PhD supervisor. He or she should act both as a coach and as a mentor to support you on your academic adventures.

Interestingly, in German speaking academia, your supervisor is still rather called „Doktorvater“ (doctoral father) or „Doktormutter“ (doctoral mother). Which implies a more family-like relationship. And which also is in line with the traditional notion of not studying somewhere at a certain university, but to study with someone, i.e., be the (graduate) student of a specific professor. And thus become a member of a specific „school.“ In the old days, the members of an academic family stood together, supported one another, helped with getting promoted, etc. Which is what you would actually also expect from a mentor. So the role somehow fits.

By the way, how are the PhD students called, I‘m not aware of a label as „Doktorkind“ (doctoral child). You are the „Doktorand“ (male) or „Doktorandin“ (female) (doctoral student) of someone. However, this is derived from the present participle of the verb meaning „to do a PhD.“ Which means, after the defense of the thesis, the label doesn‘t fit any longer. You might be a „former PhD student“ of someone, but this person doesn‘t turn into your „former PhD supervisor“ or your „former Doktormutter.“ He or she keeps the label and thus probably also the role, even after dozens of years.

Surprisingly with this family notion, at least on the side of PhD students expectation grows that these ties will last for longer — you cannot get rid of fatherly or motherly duties — and that mentoring or coaching support also will last for longer. So they tend to get disappointed when mentoring-like support stops, no information on (future) projects or even jobs are passed on, no reference letters are written any longer, and so on. Of course, one could argue that during your PhD you should also find your own way, stand on your own feet, and leave your supervision family to start your own. And of course family relationships aren‘t always positive, there is abusive behavior which is hard to report and will stay within the family. And as long as everything looks great from the outside, nobody will believe that the inside isn‘t as bright as current incidents at the ETH show.

Another aspect seems to be gender, actually. And maybe more on the side of the supervisor. Female supervisors (so the „Doktormütter“) seem to be more protective and more supportive, at least they report such actions on social media and they even use selfdescriptions as „mama bear advisor“ and the like. And from what I see (which is obviously a very small snapshot), more female supervisors state how proud they are when their current or former PhD students report success stories (an award, a talk at a prestigious venue, a good job, another grant, etc.). Male supervisors also show success of their PhD students, but with much less emotion, they rather mention this as a success story of their lab/institute/project. Which fits stereotypes of motherly and fatherly support within families, so the German terms actually are apropriate, don‘t you think?

Professor for one year (week 35): Should everybody know how to program?

This post is the report, I wrote for the GPP 2013.

Introduction

Being a computational linguist, I was trained in programming as well as in linguistics.  After school in the mid-nineties, I couldn't decide whether to focus on linguistics (or nowadays "humanities") or computer science/math (or nowadays "STEM").  So I was quite happy to be able to focus on both when studying computational linguistics.  I always loved algorithms, abstraction -- and yes, I also loved Latin.  Maybe that's a rare combination, but in today's world it turns out to be quite handy.

One aspect of the 2013 GPP motto "University and Society -- Meeting Expectations?" is the aspect of university as the institution to prepare students to be successfull in today's society.  In the last decade, we saw the emergence of more and more electronic devices, "digital" is one of the buzzwords in several scientific fields, technology becomes pervasive.  We speak of the "Generation Y" as being "digital natives."  However, if we look how today's students use technology, they are only users, they are not creators.  They often even don't know how to configure programs.

Douglas Rushkoff in his book Program or be Programmed argues that everybody should know how to program to understand today's technology and to be able to control it instead of becoming a slave of the electronic devices surrounding us.  So my personal focus in the GPP 2013 was to explore how universities support or enable learning to program.  Of course students in computer science (CS) and related fields (like computational linguistics) are trained in programming, but I was interested in courses for non-CS students.

Answers from US professors

During our visit in the US, I asked my question at two places explicitely and I got two different answers. 

At North Eastern University, Dr. Neenah Estrella-Luna, an assistant academic specialist,  as she described herself, argued that indeed, computer literacy would be a valuable topic to teach considering that university should empower students to deal with current challenges.  However, she admitted that there are no courses offered to all students, not to mention being required.  My question was understood as asking about "teaching students how to program." 

At swissnex in Boston, we met Dr. James Honan, senior lecturer at the John F. Kennedy School of Government at Harvard.  He understood my question differently and answered that students would keep faculty busy and push them to use more technology.  He talked about MOOCs before and probably this influenced his answer.  However, his statement made clear that there is a view of "computer literacy" as "being able to use devices", including the expectation that instructors offer digital content and e-learning material.

At this moment, I was a bit disappointed.  Either the necessity of teaching and learning how to program is not recognized, or, when it is recognized, it is impossible to offer such courses for all students.

While at the MIT, we visited the Media Lab and the "Lifelong Kindergarten" headed by Professor Mitchel Resnick.  We got an introduction into scratch, the programming language and online community intended to teach kids how to program using a game concept.  They learn abstraction, algorithmics, and data structures while they play with code snippets, interact with other kids around the world, and program their own games and worlds.  It's an advanced model of learning the concept of recursion while playing "Towers of Hanoi."  I was aware of scratch before and I really enjoyed seeing some demos and talking to the researchers involved in designing and implementing scratch.  I think using games as a vehicle for teaching important concepts is a good strategy -- the users aren't probably not even aware that they acquire valuable knowledge they will use later in school, in university, and in their jobs.

Situation in Switzerland

On the morning of the day I took my flight to Boston, I took part in a meeting of an experts panel on CS competencies of the Hasler Foundation in Berne.  The foundation is working towards a proposal for a general subject "Computer Science" at Swiss schools.  Currently, some schools in some cantons offer CS as supplementary subject (in German: Ergänzungsfach).  However, this subject is often taught as it was in the 1990s: students learn how to use certain software, they don't learn to program, they don't learn about abstraction, algorithms, and data structures.  In the publication "informatik@gymnasium", published by the Hasler foundation through NZZ Libro (note that the German version of this book is already sold out!), the authors argue that CS and using software are two differnt things and that school should teach students the basics of CS to prepare future citizens to cope with everyday life.  It is probably a long way to achieve this goal, but it's a goal worth all the effort.

However, here we talk about serious teaching, not about fun instruction as in the case of scratch.

Answers from the Web

After coming home, I searched the web for comments about computer literacy and opinions or activities on teaching programming.  Bill Gates, in a questions session at Microsoft's Faculty Summit, confirmed that there is indeed a "gap between how computer scientists use computers to automate their lives and how most people don't really know how to use them effectively."

Larry Hardesty talks about the "programmable world" that surrounds us and that will change the world as we know it by making the distinctions between virtual and physical objects obsolete.  To make good use of the new world, we should be able to understand opportunities and challenges (and issues) and how to manage them.

In England, efforts are on their way to teach algortithms to primary school kids.  The government acknowledges the need to "catch up with the world's best education systems."  However, this new curriculum is still under development and the teacher's union isn't sure about when would be a good starting point to introduce it -- they object to only react to governmental decisions.  According to Sean Coughlan it will include computing defined as:

Computing will teach pupils how to write code. Pupils aged five to seven will be expected to "understand what algorithms are" and to "create and debug simple programs". By the age of 11, pupils will have to "design, use and evaluate computational abstractions that model the state and behaviour of real-world problems and physical systems".

It would be great if England could manage to design and actually implement this aspect.

And there are discussions going on in the emerging field of "Digital Humanities": In a twitter post, Jan Hecker-Stampehl (@heckerstampehl) asks "Should humanities scholars learn to program or trust that the programmers in DH projects will understand them?", obviously not aware of the more than 30-year old answer, Jacques Froger gave 1970 (Froger, J. (1970). La critique des textes et l'ordinateur. Vigiliae Christianae 24 (3), 210-217.), as Michael Piotrowski responds:

Il n'est pas indispensable que le philologue établisse lui-même le programme, encore que ce soit infiniment souhaitable ; il devrait au moins connaître assez le langage de programmation pour contrôler le travail du technicien ; en effet, l'expérience m'a appris qu'il ne faut pas s'en remettre les yeux fermés aux électroniciens, mal préparés par leur formation mathématique à se faire une idée juste de problèmes concrets qui se posent dans la domaine de la philologie.

(English: It is not absolutely necessary that the philologist writes the program himself, even though it would be extremely desirable; but he must at least know the programming language, so that he is able to check the work of the technician; in fact, experience has taught me that one should not blindly rely on the electronics people, whose mathematical training has hardly prepared them for fully understanding the concrete problems encountered in the domain of philology. (translation by Piotrowski))

However, even in fields where you would expect learning to program to be part of the curriculum, it is rather rare, as the blog post by Philip Guo shows.  He argues: "If you're a scientist or engineer, programming can enable you to work 10 to 100 times faster and to come up with more creative solutions than your colleagues who don't know how to program."  Students would need more concrete motivation than only arguing that programming helps them become an empowered citizen (the argument Estrella-Luna used at North Eastern).  Guo accepts that programming tools, i.e., text editors, should be improved to foster programming, but in the meantime we should focus on teaching students programming skills to support creative problem solving.

Selena Larson emphasizes the need to teach programming to students in schools already.  She supports the Hour of Code initiative during Computer Education Week 2013, following a similar strategy as scratch: Using games and fun figures, kids should understand basic principles and get an idea about what it means to program.

Conclusion

Studies by professional assocations like the ACM (Association of Computing Machinery) regularly show an increasing number of jobs requiring programming knowledge.  They also show that there is a lack of people with appropriate skills meeting these requirements.  So there is an urgent need in society. 

As I agree that school would be an appropriate place to start teaching basic concepts of CS, university should be the place to empower students to actually program.  Maybe learning to program, acquiring knowledge about algorithms and data structures should be a required course in every curriculum.  I strongly support the statement made by Steve Jobs in an interview in 1995 saying "It teaches you how to think.  I view computer science as a liberal art.  It should be something that everybody learns."

However, we are still on the way to implementing those ideas into education, be it in school or at university.  If we have the chance to support initiatives like the Hour of Code or panels and experts groups designing curricula, those of us having the respective knowledge, should take part and see this as opportunity to serve society.

Professor for one year (week 11): What does University contribute to Society?

Last week, I participated in the Global Perspectives Programme, a joint program from University of Basel and Virginia Tech to foster academic exchange about Higher Education.  This year, the topic of the program was "University and Society: Meeting Expectations?"  We explored various aspects of "Society," "University," and "Expectations."  There are so many definitions and views of these broad concepts, that one could discuss hours and hours.  One aspect, however, is what university is expected to contribute to society.  Is it about providing solutions to current or future problems?  Is it about foreseeing future problems?  Is it about developing resources to be used for society's needs?  Is university urged to serve society and provide what society explicitly wants or to provide what society unconsciously needs?  So far, we wondered, what kind of solution universities would produce.

Most of the time, faculty and administration talked just about the questions we were exploring.  During our one-week trip in the US, the Basel group also visited Virginia Tech and had a vivid conversation with faculty of the Virginia Bioinformatics Institute (VBI).  Christopher Barrett, Scientific Director of the VBI, argued that universities would provide methods and tools to be used by society, i.e., policy-makers, to solve problems.  He emphasized that universities do not contribute solutions for current or future problems.

This statement made me wonder: At the one hand, with an attitude like this -- universities provide resources and tools to be used by others -- there is much room for basic research, i.e., research with no urgent application but that could be useful in the future.  Researchers are freed from the pressure to explicitly show usefulness in today's society.  And it makes clear that society is responsible for solving problems and for making use of the provided resources and tools.  A very comfortable statement for research, I think.

On the other hand, it reminded me a bit of the drama "Die Physiker" (The Physicists) by Friedrich Dürrenmatt from 1961 and the Manhattan Project (Einstein later regretted having signed the letter to Roosevelt in 1939 recommending that atom bombs be made).  And more so as Barrett told us that he had worked at Los Alamos before coming to VBI.  When universities -- or more precisely: researchers -- say that they only provide tools to be used by whomever, researchers implicitly say that they are not responsible for any outcome.  A researcher invents something, hands it over to the public and then doesn't care about how and by whom it is used. 

Although I appreciate the attitude to provide resources and tools rather than tailored solutions, I think universities should carefully state how to make use of their tools and emphasize the intended use.   Researchers should always take into account possible use of their findings -- the affordances -- and how to prevent criminal, inhuman, or warlike use.