Tips for a PhD defense or viva #phd

It is with quite some pleasure that I was awarded the PhD in Educational Technologies last week.

The UK version of a PhD defense is called a Viva, which resembles a closed oral examination (open book) with one external examiner (connected to another University than the one you are at) and an internal examiner (affiliated to your own University, but with whom nor yourself, nor your supervisors have co-authored a paper – so not closely professionally related). In addition to that, you have one observer (normally that is one of your supervisors, she or he will take notes on what is said, and possible recommendations) and a chair (Doug Clow, who explains all the details of the viva and who sees to it that everyone stays hydrated and in an objective state of mind). In my case the external examiner was Neil Morris (Leeds University), the internal examiner was Allison Littlejohn (The Open University, UK). The external examiner usually leads the questioning, which was also the case in my viva. Btw the central question to my PhD thesis was 'what characterizes the informal, self-directed learning of experienced adult, online learners engaged in individual or social learning using any device to follow a FutureLearn MOOC'. It resulted in a conceptual framework for informal self-directed learning, using a method that provided the voices (experiences) of the learners to come through, as such providing a theory from the ground up (in most cases a framework starts from theory, providing a top down dynamic to come to the conclusions). A draft version of the thesis can be read here. The picture shows my two supervisors (Mike Sharples and Agnes Kukulska-Hulme) and Rebecca Ferguson (who was kind enough to be my main examiner during the mock viva) and my wonderful colleagues Janesh Sanzgiri, Jenna Mittelmeier and Garron Hilaire.

The questions started off mildly (with a fair question, which aims at making you feel comfortable, so along the lines of: briefly describe your research, why were you interested in the topic you investigated). From there the questions tend to become more complex and they tend to demand a more in-depth answer. Normally the questions will start at the beginning of your thesis, and consist of overall (e.g. how did you select your literature) as well as very detailed questions (why did you select only that fragment here) which the examiner found either of interest, confusing, or lacking. This means you really need to understand why you did what you did, throughout your thesis.

These are some of the questions I got, with some additional information:  

• How do your research questions follow from your literature review? During preparation I linked all of my research questions to the most influential paper I mentioned in my literature review. This is also handy for other literature related questions, as you memorise core papers and their subsequent authors.

• Which element of your findings gave rise to the most poignant discussion; and can you list the main authors for that discussion reflecting on that part of your findings? Why did you limit yourself to these authors for the discussion on that part of X findings? I can tell you, this was a tough question. It means you relate the literature of your literature review and use some of those papers to fuel the scientific discussions on your findings taking into account what the literature already pointed to, as opposed to what your findings show to be different (or similar, as you will most likely find that your findings have commonalities as well as differences with prior research).   

• What is the relation between the research of your pilot study and the main study? In my case the pilot study had different research questions (and sub-questions) than the main study, this had to be explained, and this had an effect on the findings. This change resulted from the qualitative, exploratory starting point of my study, and the resulting findings from the pilot which urged me to rephrase the research questions of my main study a bit.

• Is there a theory runs through your investigation, and has an effect on the literature you choose to focus on, the methodology, and research instruments? In my case that was socio-constructivism, briefly: one of the theories I used (connected to the pedagogical design of FutureLearn) is Laurillards conversational framework, specifically the informal conversational framework, which is related to the socio-constructivist view of the world. Additionally, I choose to use Charmaz’s constructing Grounded Theory approach, which also is deeply embedded in the socio-constructivist heritage, and I used multiple learner voices to look at emerging codes, categories and concepts coming from multiple viewpoints (as I used multiple data sources provided to me over time by the participants in my study – participants were asked to self-report their learning through learning logs, sent at different moments throughout their learning experience with FutureLearn MOOCs.

• Questions could also be limited in scope, for instance: what is your definition of socio-constructivism? Prepare core definitions that are key to your thesis.

• How did your research questions guide your coding? Tough one, as there is a tension between qualitative research which starts from the concept of no-assumption, to research questions inevitably guiding codes (e.g. codes related to the sub-question of technology for learning).

• Or considering one area of my findings: what type of definition are you using for social learning? And how does it differ from other social learning definitions? In my case, I used social learning as it is defined by Laurillard, which fits FutureLearn, and is based on the notion of Socratic dialogue, which means it involves at least two active people. This stands in contrast with for instance Bandura (who goes back to a behaviorist view as well, as Bandura’s definition of social learning can be traced back to Pavlov), where Bandura also sees passive learning (e.g. lurking) also as a form of social learning, as it is still embedded in a the whole of society as the learning environment and is part of observing.

• Two difficult questions were raised during my mock viva. A mock viva is a sort of general rehearsal for your viva. It usually involves your supervisors, as well as a colleague who wishes you well and wants to strengthen your viva skills. In my case, I head the pleasure of having Rebecca Ferguson as my mock viva examiner and she is fabulous! I also used some of her tips in preparation for the mock viva, have a look at the top 40 viva questions she listed as important here. One of the questions she asked me was: what is the difference between MOOC learning and other online learning? E.g. active presence of a facilitator, scale, length of course versus length of curriculum, prerequisites, compulsory or not. Another difficult question was: why did not you taken into account the MOOC educators? Where the better answer would have been: I did take them into account educators, but only in the roles in which they were seen by the learners, not in their classical roles as defined by educational institutes.

Some general remarks:

Make sure you know your thesis, and use parts of it when looking for answers to the questions you are getting. I mean, physically point to your thesis, this will buy you some time to find the right answer, and will give you some additional content support.

Look confident and be succinct. This gives the idea of professionalism to your person, a research professionalism. It does not matter if you belief it, just know that you are indeed the expert on that topic, so you can and must be confident.

The questions you get can come from a variety of thoughts: interest in the approach, doubt on what you wrote, or simple trickery to see whether you do really understand what you are doing. This means that at times you might here a question, which prompts an internal voice to say “Hey! But I did do that, or I do have an argument for doing it that way!”, in that case voice your answer and do not be afraid to stick with your thesis, or correct the examiners. Of course, it is essential to always stay polite, also when you are entering a discussion. But really, the examiners are there to strengthen your thesis, so they are in a way trying to let you grasp how you can make your thesis even stronger, and you are the one who is the real expert in what you have investigated, you know the processes you used to get to your main conclusions.

Research instruments investigating Self-Directed Learning in #MOOCs #SDL

In reply of a question asked by the inspiring colleague and rising academic Bernard Nkuyubwatsi from the university of Leicester, I have grabbed my three research instruments and put them on Academia, here.

These three research instruments, or better: these three inquiry's to collect data related to my research, are related to three phases in my main study:

• Pre-course - using online survey questions;
• During course - using learning logs to capture the actual learning and reasons behind directing the learning as perceived by FutureLearn participants
• Post-course: one-on-one interviews, investigating the reflections learners have after having finished the course. 

These instruments were sent to experienced online learners that were enrolled in FutureLearn courses (three courses were selected: all from a different subject area, and organised by different universities).

As I am writing some parts of my thesis, and I still need to untangle some of the terms used referring to either Self-Directed Learning, Self-Determined Learning, Autonomous learning, Self Learning... I thought it would be good to share this already.

They are part of a research rationale which is partially shared in my probation report which you can find here ... writing updated chapters, but will take some time.

#PhD sharing #online and #MOOC #research instruments

The last couple of months I have been hammering away with data. Trying to collect meaningful answers to the question: "how do experienced online learners determine what they want to learn and how?". Well, the research question sounds a bit more formal, but I like the question this way. This central research question came out of the results from a pilot study which I planned during the first closed beta courses of FutureLearn. The pilot study is in part described in my probation report, which I uploaded in academia and can be found here. The probation report is a report you need to submit to UK based universities to proof that you are PhD material, and that you have been working on research with academic rigor and good progress for approximately 10 months.

And to add to my PhD journey, I will share the first research instruments used for my main study via this blogpost, see below. More instruments or details will follow as I proceed.

sub-questions to build narrative towards answers to the central research question
In order to get answers to the central research question mentioned above, I divided the central research question into five sub-questions, which will hopefully give an idea of the elements I investigated to come to a more complete answer:
1. What are the MOOC participants learning objectives?

2. What are the actions undertaken by the learner to attain self-determined learning goals?
     a. Who do learners connect to in order to learn?
     b. Which technologies do learners use (devices, tools and resources)?
     c. Do they mediate that learning with others or other technologies in order to add it to their learning? If so,          how?

3. What makes a MOOC learner reach further to find an answer to their learning, or what is the point beyond which they think it is not worth the effort to reach an answer for their learning objective?
4. Did emergent learning happen resulting in unexpected learning outcomes?

Quick overview of the methodology

This main study gathered Self-Determined Learning (SDL) experiences from experienced, online learners while they are enrolled in a FutureLearn course. The research consisted of three phases, leading up to conclusions on SDL in FutureLearn courses.

•  Phase 1 – expectations: gathering expectations of the participants enrolled in FutureLearn course via an online survey
• Phase 2 – experiences: collecting learning logs in which the participants are asked to describe two learning episodes every other week for the duration of the course
• Phase 3 – reflections: interviewing the participants (one-on-one) taking part in the study via structured interviews looking into the differences between their expectations and actual perceptions on their SDL as they were participating in the FutureLearn course

The FutureLearn research participants were volunteers selected from those taking part in one of three specific FutureLearn courses. The selection was based on their prior online learning experience (which could be online learning in general, self-taught learning while using the web, MOOC, mobile learning... but they needed to be online and engaged in some kind of learning for over 3 years). 

The pre-course survey questions

For phase 1 just a couple of questions were asked. The aim of the questions was to get an idea of the motivation of the participants for enrolling in that particular course, as well as to allow me to double check their previous online learning experience. 

1.       What is your prior online learning experience? (Multiple choice: no prior experience, 1 year or less online learning experience, less than 3 years online learning experience, less than 5 years online experience, more than 5 years online learning)?

2.       What type of online learning do you have experience with? (Multiple answer: MOOC, online learning, distance education course, learning experience by self-organised learning to stay on top of my field of interest, learning online from my network, self-taught online learning on random subjects, other)

3.       What is your reason for registering for this particular course (Multiple answer: professional interest, personal interest, learning need, other)?

4.       What do you expect to get out of this course? (Open question)

Although you will not find questions related to demographics here, these were in fact provided to all FutureLearn participants as part of the overall pre-course FutureLearn course survey. And I certainly did not want to double up with the survey the participants already filled in (in the past I found that saving time is essential for willingness to participate). 

Learning log template used

In phase 2 the learning log templates were the most important research instrument used in the process of this research study. All the participants were asked to fill in the learning log template at bi-weekly intervals, and to provide two templates for each 'learning log week'. The reason for pacing the learning log frequency, was again to save time for the research participants, yet at the same time get insight in their learning process. I only asked them to start filling in the learning logs from week 2 of the course, as past research into MOOC dynamics showed that from week 2 there is a significant drop in participation from curiosity based participants and at the same time an increase of participation from active participants. 

For those interested in having a look at the learning log template, have a look at this academia upload here. The learning log template consists of open and closed questions, allowing me to find quantitative as well as qualitative data.

#PhD: importance of personalisation in #qualitative research

Although I know and understand the concept of trust in online communities, up until yesterday I underestimated the effect of being among participants to allow them to connect.

In my current main study, I am investigating online learning as it is done by experienced online learners. I look at how they learn inside of the course. I ask them to fill in and share learning logs to get an idea of their informal learning as well. The learning logs also try to capture who learners talk to, or reach out to, either to find additional answers, or simply to share learning experiences. And as the first learning logs are coming in, I read them with the utmost interest and enthusiasm. The learning logs capture the learning in self-reported descriptions coming from participants taking part in three different FutureLearn courses. Although my research is qualitative, I was not fully part of the courses, not as much as I wanted to at first. But then the importance of personalisation kicked in and now I adjusted my way of research a bit.

Increasing personalisation
The first steps I took to increase a personalized approach for each of the research participants was:

• building different documents, research instruments and communications for each course. Practically this means 14 different communications, 7 different research instruments, 6 different reference documents. Not taken into account the small adaptations I made to the documents (e.g. for those participants joining the research later on in the course, requiring a different research timing).
• ensure each document, communication, or research instrument was written in a personalized way (this is not always easy, but it turned out to be definitely worthwhile)
• providing a course recognizable unique identifier for each participant, creating a visible link between a number  and the course (the unique identifier is a number used to anonymise participant data)
• address each participant with the first name they provided in the informed consent. This I do for each communication going out from me to them.
• add the unique identifier to each outgoing communication, making sure it is connected to that participant

As you can imagine, all the above measures increase the mistakes that can be made. I have been mailing about 900 communications at this point in time, all personalized - or trying to. And indeed I have been making mistakes (e.g. forgetting attachments, referring to wrong online links that are actually from other courses under investigation). Rectifying these mistakes is necessary of course, but it means sometimes participants get multiple mails, which is tough on their time schedules.

How far can you go with personalisation?
Though the learning logs are coming in, I kept feeling I was not reaching out to my research participants in a way that I could reach out to them. I felt I was missing a step. As such I strolled through the three courses of which I had participants. After having taken a look at the courses, I took another look at my research instruments (the provided learning logs in particular). Then I realized that my instruments were not personal enough. Not personal as referencing to the participants, but personal in connection with the FutureLearn courses to which the learning logs were referring too. I suddenly realized I could make them fit each course more carefully, hence making them more meaningful, more trustworthy (or that was what I was thinking).

So I went back to the drawing board, and before the last of the three FutureLearn courses started, I made sure that I personalized some of the research instruments (mainly the learning log) to make it more recognisable to the participants of that specific course. This was really necessary, as the third FutureLearn course (Basic Science: Understanding Experiments), was a hands-on course, while the other two courses I am investigating were more classic study courses (no hands-on experimenting, just understanding): one on Decision making in a complex and uncertain world, and one on the Science of Medicines.

More time, better realisations, becoming more involved

While having adjusted the documents and instruments, and beginning to get more familiar with the communications that need to go out, I was beginning to breath again. Gaining time once again. So, I had a look at the courses once again. 

This is where I realized I had not engaged with the courses the way I intended. Granted, for the course on Science of Medicines, I had planned to follow the course week on Diabetes only, as I have diabetes type 1 and I feel that all knowledge can help by keeping in tune with my illness. 

The 

So I started to engage with the courses, and suddenly I realized that this extra layer of engagement provided me with a triple return! 

• First of all I participated in the same courses as my participants, I could understand some of their remarks in more detail. 
• Second I connected with some participants, hence becoming more of a 'real' person to them. Not the distant researcher, but the human sharing experiences.
• Thirdly I learned the content of the courses, and got motivated to learn more. 

Wondering what the limit is of this type of personalization?

Did this result in an extra return for my research? 

It seems so, as people seem to respond more on my requests to share their experiences. So now I have a potential paper taking shape in my mind on the importance of being their as a researcher, also for online phenomenological research (which is what I am doing), and pointing towards possible effects of being part of the learning environment in a non-formal role, simply as a participant but for no other reason then simply taking part in the course. A bit of ethnographic research presence, but with less impact on the proceedings in the learning environment. 

Well, just sharing so I can remember once I write my thesis. Research is such a learning journey!

#PhD qualitative versus quantitative #research: trials and tribulations

Why did not I simply stick to quantitative data, the beauty of numbers in simple, straight forward formulas ?!! That scream of despair kept me awake at night for weeks. Weeks filled with hopes and doubts on getting enough data for my PhD study, eagerly looking at mails and learning logs.

Up close and personal
Qualitative research brings along much more discussions with all stakeholders, for everyone needs to be willing to share. Due to the fickle nature of language, everyone also needs to understand what is meant by the researcher when ideas are investigated. A difficult endeavour.
Quantitative research is like watching ants. You track the colony, and you get a fairly good idea of what they are doing. In a way this is what happens with learning analytics or Big Data derived from watching humans as well. You get the facts of the human colony when studying learning analytics or quantitative research, but you do not get it’s spirit, it’s drive or reasons why.
I want to know more. I want to get into the minds of people, into the minds of online learners, and understand why they are doing what they are doing, and how they do it. But this means I need to get into a conversation with them. And as we all know not everyone wants to start a conversation with just anyone.

Finding what no wo/man has found before
The setting of my research is simple enough: finding out how experience online learners learn. To see whether the assumption of us ‘grand experienced learners’ is indeed filled with online connections (personal learning network), finding what no wo/man has found before (surfing the Web), and sharing (blogging our learned reflections, ideas and thoughts). But my chosen approach could not be anything else but qualitative. It had to be laboriously gathering written data from good, willing research volunteers who’s lives are already cramped with time staking demands. Why? Because there are no quantitative holistic tools available that will capture all learning (formal and informal), and offer insights into why these data emerge.

No holistic research tools, due to no learning blueprint
The research tools of today do not (yet) permit me to simply trace or track what a learner does while studying an online course. The course related resources can be tracked of course, but there is so much more that some of us do: connect with others (partners, face-to-face colleagues) to find solutions related to the course, gather extra information connected to the content of the course as well as our own contexts for the topic. Granted, xAPI provides some self-reported informal learning diaries. Twitter and blogs offer idea and diary options so personal learning reflections can be shared. But there is no holistic learning tool available yet. This is due to the fact that at this point we can only assume how people learn in such online environments (especially regarding informal/formal learning actions). So as long as we do not know what learners actually do to get to a full understanding of an online course in relation to their personal learning needs, no tool will grasp all that is needed. This of course provides a solid rationale for my research: getting a blueprint of how people learn in an online teaching environment.

Time is of the essence
Which brings me back to the tension between qualitative versus quantitative research. Up until now, and for this type of exploratory research the qualitative research options seem to be the best option to get an idea of how experienced online learners learn.
However, I do get the impression that all of us are losing out on time. Our time is under pressure. We work, have hobbies, care for our families, … and now with MOOCs rising, we learn as part of our lifelong learning or leisure learning realities. And this is where my research comes in, with yet another demand on precious time of learners that simply want to get on with life. This lack of time, manifests itself in people willing to engage in research, but finding they simply cannot do it (if they want to stay sane and on top of their lives). Some volunteers get slightly cross due to questions that they find irrelevant, others interpret the words I write in a different way than they were intended… and all of them have a point, as language is fluid, as is any meaning making. So, as a researcher I listen and try to find adaptations that can make life easier for my willing research volunteers. This is not always an easy task, but I owe it to them to try and make it work, or to make participation in my research easier.

Big Data or Big Emotions?
So now, with questions and discussions rising (on questions and instruments used), inevitably emotions come into the research and into the hearts of all volunteers. Inevitably people discuss ideas, have an opinion on what is asked and they feel positively or negatively inclined towards what is asked from them.
Sometimes this gets to me. I really want people to get a positive feeling from sharing their experiences, and in turn use their experience to provide guidelines to new learners, as well as insights to course facilitators and teachers. That way we all grow, and – hopefully – make learning a more intuitive, natural act. The way it is supposed to be.

Luckily most volunteers know this, they put their efforts in, knowing it will help others. And I am deeply indebted to all of them. The data keep pouring in … I am grateful.

(Another great cartoon by Nick D Kim: http://www.lab-initio.com/)

#PhD journey: preparing main study #MOOC

The next big step in my PhD journey is coming up: the main study. Once September comes, I will hopefully get massive amounts of data coming my way (well, lets say massive yet controllable data would be ideal, not BIG data, rather meaningful data in manageable abundance). Rolling out a main study is more difficult than organizing the pilot study for multiple reasons: personal knowledge (by knowing more, additional reflections come to mind when planning an follow-up), getting more people to agree that I come and gather a flock of research participants, making sure all questions will lead to meaningful research...

My previous steps during my PhD journey were:

• writing a probation report (which included my pilot study set up, some literature and rationales for the research choices I made at that point in time)
• considering the pilot study data analysis and filtering out key findings (e.g.what influences MOOC learning, what is of importance for learning what is not, is there a difference in learning depending on online learning experience...) that were of use to my upcoming main study (I will put these into a more legible document in the upcoming weeks)
• rewriting my central research question and following sub-questions
• building my research instruments (which in my case are questions I will ask the research participants: keeping learning logs, engaging in interviews)
• and of course, very important for a PhD: rationales for each step. 

Research focus

For my research I look at experienced online learners (adults in most cases), and how they self-determine their learning (this links to heutagogy, I wrote briefly about the why of this approach in an earlier post here). There are multiple reasons why I like this: relevance to lifelong learning, adult learners can be more self-determined due to their own experience or professional/personal needs, it is advanced learn-to-learn combining personal goals with digital skills with a mediation linked to critical thinking (which content do I find of interest, of all the discussions I am engaged in - who do I learn from, which argument do I feel is more to my liking...). This emphasis on experienced (adult) online learners immediately opens up the MOOC space for me, it brings it back to its first roll-outs (cfr. CCK2008) and it relates to what young as well as adult learners do in terms of 'internet use for learning': you want to find a solution for something, you connect through the internet (tools, objects, people), you surf the net, you connect with others, you make curate in your mind what is useful, and assemble the information into new knowledge (well, that is how I think it goes, but a lot needs to be investigated). An adult learner makes decisions for their learning, they make their own decisions based on their own expertise (I assume here): we all have our own agenda's, and as such we need different bits of information (chosen drops from the Internet fountain or our own networks). Of course in this learning chaos, there might also be emergent learning happening, no matter how experienced one is as a learner, and this is of course also of interest (how does it work, might it become integrated in durable learning...).

So my central research question is: "How do experienced online learners manage self-determined learning when engaged in a MOOC in order to attain their learning objectives?"

Research environment

In order to investigate this, I was looking for research participants that would be engaged in MOOCs that would attract or support that type of learning. And I wanted MOOCs that had different feels to it as well, or could attract different populations that would (possibly, hopefully). I was also looking for MOOCs that would take more than two weeks, as research shows that there is an interesting chasm in interaction between week 2 and 3 of a MOOC. And as I am part of The Open University and its partners, I have the pleasure of being able to ask MOOC organizers from different universities that are all part of FutureLearn  to see whether I have their permission to gather research participants from their MOOCs. 

The world of academics is amazing, as I got three agreements of the lead facilitators of each MOOC I was interested in (SO GRATEFUL!). I gladly share the three MOOCs here:

The Science of Medicines: learn the science behind how and why medicines work, and what can improve the patient treatment experience. This MOOC is organized by Monash University in Australia, and lead by Ian Larson. The Monash University is a leading university for pharmacy and health courses, and I really look forward to the course. I choose this course as it was health related: building on past experiences I would think a lot of health professionals might be interested in this course as it might provide extra insight into medicines and pharmacy. The course also provides support for carers and people with diseases mentioned in the course. This is an additional bonus, as my pilot study showed that health issues can be a reason to follow a MOOC. And I am a diabetic type 1 (= insuline dependent, so interested in that health part as well). 

The course starts 1 September 2014, and lasts for 6 weeks, with a 4 hourse pw study time. 

Decision Making in a Complex and Uncertain World is my second MOOC of interest. This course will teach us the first principles of complexity, uncertainty and how to make decisions in a complex world. It is organized by the University of Groningen in the Netherlands and Lex Hoogduin is the course lead. The reason for choosing this MOOC to look for research volunteers was based on its content related to complexity. For MOOC learning, and especially experienced online learning has a lot to do with dealing with complexity. As such, I thought it would be interesting, and I hope to see some parallels coming out of the content, and the learning reflections. 

The course starts 15 September 2014, lasts for 6 weeks, and has quite a hefty 6 hours per week study workload (which is of interest as well, as high expectations sometimes provides high effort return). 

Basic science: understanding experiments is a hands-on course which introduces its participants to science-based skills through simple and exciting physics, chemistry and biology experiments. It is organized by The Open University, and lead by Hazel Rymer. This MOOC offers a different learning set-up: it is more practical, as course participants are asked to try out experiments in their own home (one of which is: getting DNA !). So this might ask different learning to occur. 

The course starts on 22 September 2014, lasts for 4 weeks, and has an estimated study workload of 3 hours per week. 

Excited by the prospect of getting people on board for this research... so will post as the next steps are ready. 

Writing social sciences #research paper #phd

Again reaching for content that helps me keep an overview when wanting to write a paper. The best online resource for me is still the one provided by the University of Southern California. It provides so many useful tips for each section, just have a look and see for each section of a classic research article. So thanks to USC libraries ( @USCLibraries )Even just reading through their brief description of each section gets my mind oriented:

Abstract

An abstract summarizes, usually in one paragraph of 300 words or less, the major aspects of the entire paper in a prescribed sequence that includes: 1) the overall purpose of the study and the research problem(s) you investigated; 2) the basic design of the study; 3) major findings or trends found as a result of your analysis; and, 4) a brief summary of your interpretations and conclusions.

The Introduction

The introduction serves the purpose of leading the reader from a general subject area to a particular field of research. It establishes the context of the research being conducted by summarizing current understanding and background information about the topic, stating the purpose of the work in the form of the hypothesis, question, or research problem, briefly explaining your rationale, methodological approach, highlighting the potential outcomes your study can reveal, and describing the remaining structure of the paper.

The literature review

A literature review surveys scholarly articles, books and other sources relevant to a particular issue, area of research, or theory, and by so doing, providing a description, summary, and critical evaluation of these works. Literature reviews are designed to provide an overview of sources you have explored while researching a particular topic and to demonstrate to your readers how your research fits into the larger field of study.

The methodology

The methods section of a research paper provides the information by which a study’s validity is judged. The method section answers two main questions: 1) How was the data collected or generated? 2) How was it analyzed? The writing should be direct and precise and written in the past tense.

The results

The results section of the research paper is where you report the findings of your study based upon the information gathered as a result of the methodology [or methodologies] you applied. The results section should simply state the findings, without bias or interpretation, and arranged in a logical sequence. The results section should always be written in the past tense. A section describing results [a.k.a., "findings"] is particularly necessary if your paper includes data generated from your own research.

The discussion

The purpose of the discussion is to interpret and describe the significance of your findings in light of what was already known about the research problem being investigated, and to explain any new understanding or fresh insights about the problem after you've taken the findings into consideration. The discussion will always connect to the introduction by way of the research questions or hypotheses you posed and the literature you reviewed, but it does not simply repeat or rearrange the introduction; the discussion should always explain how your study has moved the reader's understanding of the research problem forward from where you left them at the end of the introduction.

The conclusion

The conclusion is intended to help the reader understand why your research should matter to them after they have finished reading the paper. A conclusion is not merely a summary of your points or a re-statement of your research problem but a synthesis of key points. For most essays, one well-developed paragraph is sufficient for a conclusion, although in some cases, a two-or-three paragraph conclusion may be required.

Plan to move from #quantified self to Qualified self

My ultimate scientific breakthrough dream would be the Qualified Self in the analogy of the Quantified Self. The Qualified Self as a state of being, enabling to be a more qualified human. All the gathered data would gather data on: emotions, creativity, understanding, progress, personal character... data and characteristics that occur in most humans from all areas and backgrounds. And with Utopia on my mind, I would love to become a member of the Qualified Self movement, if this is a movement build on accepting the differences that we all have. It is a natural thing, each of our brains has neurons connected in different ways, nevertheless we all have emotions and maybe once the Qualified Self movement is at full speed and development, we - humanity - will realize we are all just the same as we are all different, and that is a good thing. War would end, conviviality will be natural, equality in difference will be achieved for all genders, races, and abilities. And while achieving this, we will have learned from each other, from the qualified data that will be available and from the actions we must all undertake in order to reach all the benefits coming out of the qualified data and establish a society that is prosperous.

Maybe this sounds a tiny bit unrealistic, but hey, it might happen! So here is my plan!

Quantified self as a starting point
So I am looking at what is available (quantified self), and build upon this to start the qualified self option.
With Big Data pushing its way into every niche of society, one of the more individually lived experience with big data is the Quantified Self (QS). The quantified self movement is a movement to incorporate technology into data acquisition on aspects of a person's daily life in terms of inputs (e.g. food consumed, quality of surrounding air), states (e.g. mood, arousal, blood oxygen levels), and performance (mental and physical). Such self-monitoring and self-sensing, which combines wearable sensors (EEG, ECG, video, etc.) and wearable computing, is also known as lifelogging (Wikipedia, 2014). The quantified self movement proclaims that the data will ensure a better (physical) world for everyone. Who knows, the ideas and hopes of the Quantified Self will be realized: better health for all (e.g. Ari Meisel who learned how to control his Crohn's disease through the use of data), a better sleeping pattern for all (for those interested in tools look at this list, or the tools provided on the QS website (one section is on mood)), or in general getting a better understanding of the human physical body and world.  But at the end of the day it is just quantity, it is not about Quality and to me

My quantified life as a diabetic
As I am a diabetic type 1 (insulin dependent) I have a bit of experience with logging some part of my physical being. With an average of measuring my blood sugar 6 times a day, I track my blood glucose. I did use a continued glucose meter (CGM) in the past, and the constant, live streaming detail surely made my life easier keeping track and understanding the impact of various food intakes. But as the CGM was quite expensive, I switched back to strips and blood sampling to keep on top of my blood-sugar levels. Does this measuring improve my life? Yes, it surely does. And this results in a better quality of life as well, but ... measuring physical data only goes so far. I am more than my body, I am mind. So I want to understand more.

Shifting from quantity to quality
There is also an interesting development based on statistics coming from the Quantified Self embedded in the professional workspace. One such example is Fitbit data, a Japanese experiment to map workplace relationships (professional relationships that is) by providing pedometers to workers and analysing the data coming from those pedometers.
And although efficiency sounds wonderful, it does not necessarily align with the thought of life's quality. And it is that quality of life I am interested in. For let's be honest, if technology keeps moving forward, automation will take over most jobs as accounted by many articles and experts, and in the end we - as a society - will have to rethink work, financial transaction, leisure time and getting

Setting up first trial for qualified or qualtified self instrument for measuring learning
So the first step I want to take to make use of the technology for a more qualified self improvement, is to build a mobile research instrument that measures learning. I need it, as I am investigating all the factors that influence self-directed learning in MOOCs for learners using multiple devices and engaged in individual/collaborative learning. I hope to come up with a mobile app that will make it easier for the learners to share their learning track and ... keep track. I know there are personal learning lockers out there, but still I want to see what I can come up with, ideally something so simple it becomes beautiful (quality yes).

Stephen Downes added a more appropriate word for the instrument: the qualtified self. I can see this as a next step between quantified self and qualified self, and due to the human difficulty to surpass the wish/need/capacity for metrics instead of matrix.

A critical lens on the quantified self

After doing some initial exploring, I stumbled upon an article with solid critique on the challenges of the qualified self as it is designed now. The article was "A dream of a feminist data future", a great essay written by Amelia Abreu in whch she puts wonderful, intelligent questionmarks on each step of the quantified self movement saying that Women’s lives have been subjected to quantification for decades, and how this is not always for the good of womankind. Amelia takes the reader through history of data handling (mostly a women's job at first) and puts an important factor into the equation (also raised in software development): "The Quantified Self movement searches for universal points and scores and payoffs, but doesn't acknowledge the systems behind how those are valued, who chooses them, what they mean, and who they leave out -- often the already overlooked and marginalized, like caregivers and other low-wage workers."
Amelia concludes with the question whether "we can ever reach a point where sensor technology and data-mining can be accessible and successful, flexible enough to be genuinely empowering, allowing users to control their own narratives".

So Amelia's article provides me with an additional point of interest. Will try to honor it and find an empowering angle to my app, at least it will be used to track learning, which to me is part of anyone empowering themselves. As always ideas are welcomed and joining hands are appreciated. 

20 strategies for learner interactions in mobile #MOOC

Let's be honest, we all LOVE research *grin*, or facts, or lists, or useful practices ... or practical strategies for that matter. Well, here is a new set of useful strategies for mobile MOOCs, I hope you like it!

In my latest research I focused on the impact of mobile access on learner interactions in a MOOC (Massive Open Online Course). The research was done to get my Master in Education at Athabasca University. As always all of the Athabasca faculty was supportive to get the research up to their standards (ethical approval, relevant literature...).

The readable and hopefully useful list of 20 mobile strategies to increase learner interaction in a MOOC that came out of my research can be found below in this post, but feel free to read the full thesis here, it has links to ethical procedures (e.g. informed consent form), some web analytics, community of inquiry use to screen learner interactions.... If you want to reference to the strategies, or parts of the thesis, this is the APA reference for it:

de Waard, I. (2013). Impact of mobile access on learner interactions in a MOOC. Retrieved from Athabasca DThesis database http://hdl.handle.net/10791/23 

Abstract of the research 
As mobile access and massive open online courses (MOOCs) become a global reality, the realm of potential distance learners is expanding rapidly. Mobile learning (mLearning) as well as MOOCs are based on similar characteristics as shown in the literature review of this study. They both enhance a community feeling, increasing networking and collaboration; they strengthen lifelong and informal learning, they use social media to a large extend and they are ideal for setting up communicative dialogues. The focus on learner interactions is of interest, as research has shown that dialogue is an important element for learning and knowledge enhancement, and mobile access increases the opportunities to enter into such interactions. This thesis study used a sequential explanatory mixed methods approach to investigate the impact of mobile accessibility on learner interaction in a MOOC. The study showed that opening up a MOOC for mobile access has immediate impact on learner interactions, as participants with mobile devices tend to interact more with their fellow learners in comparison to their non-mobile colleagues. This was deduced from the mixed methods approach looking at web-based statistics, an online survey, an analysis using the Community of Inquiry framework and one-on-one interviews with volunteers. The study formulated a set of 20 strategies and possible consequences deriving from the analysis of the impact of mobile accessibility in a MOOC and more specifically how this affects learner interactions. These strategies might optimize the impact of mobile access on learner interactions in an informal, open, online course. Future research needs to support the findings, embracing a larger learner population from a more varied background. Overall, this research hopes to add to the body of knowledge strengthening the field of distance education.

List of 20 mobile related strategies to increase learner interactions in MOOCs:

Design
1. Offer a ubiquitous learning environment based on BYOD design and content, making use of existing ubiquitous tools (social media, e-mail…) so people can switch between devices at their own preference.
2. Create a user-friendly, one button centralized access learning environment. This easy access must be linked to a clear course overview to increase transparency, user-friendliness and provide the learner with a structure that s/he can organize for self-regulating learning purposes.
Self-directed learning 
3. Provide self-directed learning strategies to the learners.
4. Enabling immediate access to content material as well as discussion areas adds to time management options and it enables self-regulated learning.
5. Offer synchronous and asynchronous learner activities within a clearly timed course. This provides the necessary freedom for the learner to access, reflect and possibly react on the subject touched at specific moments during the course.
6. Provide a clear timetable of the course, while embedding time for reflection into the course timeline. This suggested flexible, yet cohort move through the course provides an opportunity to nurture reflection time, which is in direct relation to learner interactions.
7. Embed informality in the course to allow increased, autonomous learner interactions to emerge. This room for emergence is induced by the course being both formal and informal, or informal overall and being mobile. The informal character of a course results in participants feeling more at ease with sharing and producing content and engaging in interactions across all their devices.
Digital skills
8. Increase the necessary digital skills of the learner, providing basic training before the course starts via meaningful content-related actions. If a course is accessible for a multitude of devices, it affects (the need for) digital skills, because multiple devices have multiple characteristics and affordances.
Content 
9. Offer an array of course materials, varying from bite size snacks to big, time consuming content. The mobility of the user results in the ability to access materials in a variety of locations and times. As such a wide array of course materials is needed to cater to the time availability of the learner. Offering the learner a choice to tailor the content to their current possibilities.
10. Provide a sense of ownership about the content and the learning: BYOD, contextualized options, this adds to the overall learner motivation.
Human learning environment
11. Ensure a safe learning environment. This essential to increase learner interactions in general. Tolerance, trust, daring to write in a non-native language and knowing that one can pose every content related question and not being judged for either its simplicity or format must be set early in the course.
12. Provide interaction/communication guidelines stipulating balanced communication allowing a safe discussion area to be ensured. By creating a safe learning environment, a broader perspective of personalities are tempted to engage and interact in the course.
13. Profile a central course person(s) (e.g. central coordinator, course support person) who watches over the interactions and links to each participant personally, ensuring a trusting learning environment with room for cultural and language diversity.
14. Watch over the group-size. Community feeling is increased by an intermediate group-size and learner-centered activities, which in turn affects learner interactions.
15. Allow networks to emerge. A community feeling based upon easy (mobile) access increases the formation of a more durable professional network for those connecting to each other in a way that surpasses the course duration.
Course activities
16. Embed icebreaker activities and/or discussions at the beginning of the course to allow learner interactions to take off. These activities should also be linked to intellectual topics.
17. Ensure discussions or conversation starters. The act of conversation and exchanging ideas leads to more interactions as participants become more familiar with each other on professional grounds.
18. Create meaningful, contextualized, generic, topic related interactions, as they are pivotal to create a course community spirit, because the exchange of professional interests adds to the knowledge need of the learners.
19. Add activities involving non-verbal communication to offer additional understanding, which increases the community feeling, for it might offer an additional insight into dialogue and discussion.
20. Ensure topic relevant learner diversity in examples or actions. Learners can more easily join in those conversations where they detect knowledge niches to which they can provide an answer, strengthening each other.

#qualitative research: introduction to #grounded #theory and some emotive language use in courses idea

The last couple of months I have been immersed in data analysis research: some qualitative (argh) and some qualitative (well, argh as well). No matter how I twist and turn it, I need to really dig into data analysis to understand how it works and why this type of analysis is a good thing. Let's just say my mind is not naturally equiped for data analysis, although on the other hand I get somewhat of a high when an analysis is done. My MobiMOOC Research Team (MRT) colleagues and I have also been working on a paper regarding emotive language use in an open online course, in order to deduce whether some sentences could be indicators of emerging demotivation and even dropout of an open course. For if this would be the case, course facilitators and even computer data mining programs could be used to pick-up this effechttp://www.blogger.com/img/blank.gift and enter in a motivational conversation with those learners (nice idea). This experience got my eyes opened as I understood how less I understood of qualitative research.

So for all of you that are also struggling, I will gladly share any meaningful resources that might just get us a bit more knowledgeable on data analysis. Starting with one of the most commonly used one's: grounded theory. I found this set of videos on YouTube, these are lectures given by Grahttp://www.blogger.com/img/blank.gifham Gibbs, who teaches at the University of Huddersfield (United Kingdom) and with a typical British under-cooled flair (how do those Brits keep their passion so controlled?) he gets the key points across: coding, ethics, approach...

Below you will find the first (5 min) video, but you can also subscribe to the Qualitative Research Channel right here and get all of the videos on the subject.

Btw if anyone knows great resources on data analysis, feel free to share, I can use every bit of it. Now diving back into Creswell (2007) on qualitative methods design.