Categorie archief: English Jam
Data Ecosystem Infrastructure – BSc graduate assignment
Again I have the opportunity to have some graduate students helping me with my PhD. One of them is Eddy. But he will introduce himzelf in this guestblog:
Data Ecosystem Infrastructure LA4LD
(original posted on: https://eddyvandenaker.com/post/Data-Ecosystem-Infrastructure-LA4LD)
Hi, my name is Eddy van den Aker and I’m currently doing my graduate internship. My project is part of Marcel’s PhD research project (https://2bejammed.org/2017/01/03/the-basics-of-my-phd-research/) about learning analytics, learning dashboards, and learning design.
One of the problems faced by faculties, course designers, and teachers is the lack of insight into the student experience. Faculties are rated based on two factors: the time it takes for students to get their degree and the student experience. The first factor is obvious and easy to measure, but student experience is harder.
Currently the faculty of ICT within Zuyd University of Applied Science has two ways of measuring student experience. The first is the Nationale Studenten Enquête (NSE), which is a national questionnaire filled in by students from all Universities (of Applied Science). The second is a questionnaire at the end of every course, these are faculty specific.
The results of the NSE are not linked to a specific course, and the course questionnaires are done after the course has ended, so the results also come after the fact. The feedback toward the students on what is done with the results is also limited, which probably (based on anecdotal evidence) contributes to lower participation numbers. All in all, not enough data is available to improve student experience, and students are not seeing enough actionable feedback to be more engaged with the courses and the faculty.
To solve this problem Marcel has suggested creating a data ecosystem in which students, teachers and course designers participate to collect and make use off more and more useful data. Several projects have bin done and are currently going on to develop systems to collect data (for example the IoT projects https://2bejammed.org/2018/01/02/5-student-teams-working-on-classroom-iot/). Another project is looking at ways to present the data gathered in a collection of dashboards (LINK NAAR SANDERS POST).
My project fits neatly between all projects mentioned before. I will be developing an open-source infrastructure that can catch, clean, structure and store all data gathered while also delivering the underlying services needed to present the data to the users through dashboards.
Because this system sits at the core of the data ecosystem and must be able to support many different kinds of systems, both current and in the future, it is vital to make the entire infrastructure modular. During my internship, a couple of modules will be developed.
The first module will be an end-point for collecting information on student attendance. This system could be an RFID reader on which students swipe their student-card. Another module connects to the digital learning environment, in this case Moodle and collects data on how students use the provided course material. A third module imports student results from a file. And finally a last module will collect and store data from questionnaires.
As said before it has to be possible to develop more modules later down the line, adding for example environmental variables from the classroom or students study room at home. Another example would be to track the view of students in the classroom, where they are looking on the slides, what draws their attention.
Any system that collects this amount of data, especially potentially sensitive private data, has to consider the privacy of it’s participants and thus the security of the system. A way has to be sought to ensure that no one but the student themselves are able to see their own personalized data. Teachers and course designers will only see anonymised group data. The general security of the system also has to be considered.
During the development of the system I will be using a couple of different methodologies.
Design based Research Process
This project will be using the design based research methodology The first three phases (problem definition & motivation, objectives of solution, and design & development) will be completed during the project, the fourth phase (demonstration) will be started.
Systematic Mapping Review
At the start of the project, a systematic mapping review will be done to see in which fields data ecosystems have been suggested and maybe even deployed. It’s also interesting to know if any effect studies have been published in cases where data ecosystems have been deployed.
Scrum and GitHub
For managing the project I will be using a slightly modified version of Scrum. Slightly modified because I’m the only person in the development team. For tracking all Scrum related information I will be using the issues, pull requests, projects and wiki pages on the GitHub page for the project.
I wanted to figure out how to automate the entire Scrum workflow on GitHub. I have made some decent progress on it, good enough for this project, but I still have to move “to-do” items manually to “in progress” and after that to “in review”. If you read my post on converting exam questions to flashcard (https://eddyvandenaker.com/post/Converting-Exam-Questions-to-Flashcards/), you know I’m lazy (in a good way, I hope) and I will be looking to automate as much as the workflow as possible, so maybe I can find a solution to these two manual actions.
Test Driven Development
For the development of the system I’ll be using Test Driven Development (TDD). The basic idea of TDD is to make testing an integral part of the development cycle. By developing automated functional and/or integration tests first, then developing smaller unit tests. at first these tests should fail (it would be weird if they didn’t). Only after having done all that, you write just enough code to get the tests to pass (or at least progress to the next step). When you have some passing tests you can refactor (improve) the code while using the previously passing tests to make sure the program does not regress. This process is often called Red, Green, Refactor.
My internship lasts half a year (20 school weeks). The first 3 weeks are spend on clearly defining the project, choosing the methodologies, and planning the phases of the project. Week 4 and 5 are used for requirements analysis and the systematic review. From week 6 until week 16 the system will be designed & developed in a couple of Scrum sprints. The last 4 weeks are used to prepare for the presentation at the end of the internship and to finish up the project in general.
The design & development phase consists of a number of sprints:
- Setup (software architecture & base functionality like logins, database connections, etc.) – 2 weeks
- Importing student results from file – 1 week
- Student attendance – 1 week
- Moodle/xAPI connection – 3 weeks
- MSLQ or other questionnaire connection – 1 week
- Admin panel – 2 weeks
- Wrapping up (extended testing, deployment considerations, etc.) – 2 weeks
In about 5 or 6 weeks I’ll be posting a status update on where I’m at with the project. Another 5 or 6 weeks after that I will present my results. Finally when I’m (almost) done with my internship I’ll write a post about my experiences.
The repository for this project can be found on https://github.com/eddyvdaker/Zuyd-LA4LD-Dataecosystem
 Peffers, K.; Tuunanen, T. (februari 2006). The Design Science Research Process. Opgeroepen van
wrsc.org op 26 februari 2018 via:
Klik om toegang te krijgen tot 000designscresearchproc_desrist_2006.pdf
 Kitchenham, B. (2007). Guidelines for Performing Systematic Literature Reviews in Software Engineering. Opgeroepen op 14 maart 2018 via:
Klik om toegang te krijgen tot 525444systematicreviewsguide.pdf
5 student teams working on classroom IoT
In the previous post (link: post is in Dutch) I told you that currently five teams of students are working on an IoT for learning activities proof of concept. IoT, for those who don’t know, stands for Internet of Things. With smart sensors, existing data-indicators and controllers, environments are being monitored and possibly influenced as smart as possible. In the course we are running now three different cases are presented: “Assisting elderly and their care-takers in living longer in their own home by IoT”, “Management of spaces, energy, costst of large (school)buildings”, and “LA4LD IoT”.
I am involved in all the groups but as a customer specially involved in the LA4LD IoT solution. Students should present to me a proof of concept that is applicable at Zuyd or the student’s home at the end of the course. I have five teams of students working for my assignment and I am very pleased by their enthusiasm and ideas. Students are in the proposal phase of the projects and after the holiday they have to build their proof of concept with IoT gadgets and tooling. At the HBO-ICT they don’t have to solder but can use solutions like the raspberry PI, Z-Wave or other domotica sets commercially available. They have to learn using, adapting and implementing them in the environment of the customer and then find a smart way to cope with the data or visualize the data according to the wishes of the client.
In my case they have to take into consideration that the things I want to have measured are either needed to improve the learning activities for all students or the learning process of an individual student. Some of them even embrace research and solutions of environmental control (i.e. a room with a specific kind of temperature is most suited for learning efficiency). I’d like to give you a first glimpse of the ideas of two of the five groups.
Remember I specifically asked for a proof of concept that would be implementable in one or more rooms at Zuyd University before 1-9-2018.
Table 1. Idea: IoT in higher education
In the first example a classroom setting is chosen. The actions and possibilities are described in Dutch but the idea is clear. Fun part is that they have chosen to implement a Bluetooth beacon and that I had to stop them in multiple occasions, because they were coming up with ideas to use that Beacon to improve the interactive experience in the classroom. Great examples and definitely worth further exploring, but in my case it is about getting information on the learning activities designed and the learning process at hand and trying to improve both by informing students and teachers.
Table 2. Idea: IoT in Higher Education workplace of a student
In the second example I got surprised by the students, because they took up the challenge of looking into the ‘private’ workspace of the student at home (or in a library). They came up with a desk-setting and a tooling (which name I shall not tell at this moment) that is portable and can be moved and set up anywhere.
Both groups have made a bigger presentation on what they are going to do with the data and how the data indicators relate to either learning activity design or the learning process.
The groups are half way their course and still have some time to work further on this. I already bought a Raspberry Pi, a Pi cam and some sensors (still have to be delivered) so that they actually can build a set up for me to use.
I hope that the students find a way to connect with the great research done on these topics. My colleague at the OU, Daniele di Mitri (http://hdl.handle.net/1820/7525 http://hdl.handle.net/1820/7951) and former colleagues Jan Schneider en Peter van Rosmalen (http://hdl.handle.net/1820/5571) have done several experiments on sensors, multi-modality and learning. Not always to get a broader picture of the design of the learning activity (sometimes on the learning process or learning experience) but worth of reading and using. Let’s see if some of them read this blog ;).
In my LA4LD project these are the first steps in this area. As we have a lector smart devices and a lector Internet of Everything at Zuyd there are a lot more steps to take. I am looking forward to tell you about them.
With kind regards,
Opportunities and Challenges in Learning Analytics for Learning Design
Wouldn’t it be great to be able to give insight in how students perform and like a certain learning activity? As a teacher I often ask them how did they like several learning activities during a coffee break or one of our social activities. As an institution we ask them after an Educational period of 10 weeks with a survey. This survey (called blokenquete) has one question in it about all of the learning material in the course. Besides the extra comments students can make in general there isn’t a question on separate learning activities.
Wouldn’t it be great to be able to give teachers but also students insight in how students perform in and like the learning activities they are provided. I believe that this would help students to increase performance and results and that teachers and institutions are able to provide better fit learning activities. Now often I only use my gut feeling or the “results” of conversation with the students at the coffee machine. It would be great to have an extra instrument, which based on data and input from students generates information on a learning activity level.
In a literature review, soon to be published and presented at the EC-TEL 2017 conference, I searched peer reviewed journals for experiments with learning analytics for learning design. I have been trying to identify opportunities and challenges in this field. I found it in three opportunities, who each have their own opportunities and challenges within them.
- Using on demand indicators for evidence based decisions on Learning Design
- Intervening during the run-time of a course
- Increasing student learning outcome and satisfaction
In the table below I have categorized the articles I found into the three opportunities and the subopportunities and subchallenges within. I have specific tried to look for experiments and research on Learning Design, Learning Analytics, Learning Dashboards and Metacognitive Competences. This last item is interesting to me because from a Knowledge Engineering perspective the topic of “learning to learn”, which we do as we use our meta-cognitive competences is especially interesting. Taking these competences into consideration while designing a Learning Analytics for Learning Design solution seems very relevant.
After publication of the article in the Conference Proceedings I will put a link to the article here and if my presentation is ready the slides will be available here to. For now I can only share the keynote presentation of Prof. Dr. Hendrik Drachsler who is my supervisor. He talks about the place of my and my colleagues research in the world of Learning Analytics.
After this literature review on the state of the art I am going to try to design a Learning Dashboard which incorporates a Learning Analytics for Learning Design solution, which can give information during the course and which tries to make the most of the meta-cognitive competences of the students.
If I project the opportunities and challenges on a model for a solution you will get the next figure:
I will keep you posted. But feel free to ask.
Why do we want blended learning?
Thanks for your last blog about “How to support educational teams with the design of education.” It again illustrates the difficulty of the implementation of blended learning. In the picture you use, several elements are mentioned which have influenced succesfull implementation strategies. In that graphic I miss the element of the management.
No, this is not a blog in which I want to complain about the management. I just want to try to see their point of view on this case of trying to get more blended learning. A case, sometimes struggle, sometimes a nice match, in which we have had several roles in the last few years. Their point of view is: why do we want blended learning, and do we as teachers, students and thus management want blended learning?
Quality of education is the dreamed answer, Sir Ken Robinsons’ vision of education that is not factory, which we were able to see on stage, is a wish waiting to come true. Self regulating collaborating networks of teachers and students co creating educational ecosystems is still science fiction. The managers want to dream, wish with us, they want to make science fiction into reality as well. But most of all they are stuck in that reality and sometimes we seem to forget.
The questions we have to answer aren’t how can we implement Blended Learning, as you said it yourself, that we know already. No we have to find the definite answer why do we want blended learning?
As long as we can’t translate our dreams and visions into profits, turn-over, student succes rates, student satisfaction, teacher satisfaction or regionwide knoweldge increase, management will have too much other priorities. So our real case is to illustrate the evidence there is in the context we are in so that management can dream, vision and make blended learning reality with us.
I believe that if we do, they will have a lot of fun doing it with us.
As a PhD student I will do my share, at least I will try, but I need allies. As a Zuyd community we will have to collect evidence. Not to defend our own faculty or our own model or framework which we have chosen, but to create an environment that is able to use the qualities we have to evidence based adjust our education to the demands the context or our working environments need.
Let’s go to sleep and dream on that.
Good night, Marcel Schmitz
Second week of #BlendKIT2017: Act to interact
As promised another moment to blog on the BlendKIT course activities I am doing. After reading chapter one, which I gave my comments on in the previous blog on BlendKIT2017 I also had to do some Do It Yourself assigments. I delivered a Course Blue Print and mix map for a course that I am making blended. The Blue Print gives an overview on al the course activities, course goals and course description. The discussion on learning outcomes (in Dutch) still is going, but for now I’ll manage. The Mix Map is a combination of Face 2 Face and Online elements and forces you to be aware when to choose which form.
The reading of week 2 is on blended interactions. In this chapter a brief summary on interactions within a blended course is given. As I am trying to redesign my current course: Design Science Research, I need to keep in mind at every step how I want to interact with my students or how that I hope that the students will interact (with me/each other). Design Science Research isn’t the first topic students have in mind when they want to receiver their ICT Bachelor degree. So in the first steps of my course I need some inspiring interactions. Perhaps by experts in the field with great research examples. If the expert is an inhouse expert I can make that meeting face to face, but if it is an internationally known expert I can also consider to do a online Q&A.
Thinking about interaction all the time and designing all the parts with interaction in mind let’s us also think on which role we want to play in our course. From the literature several models are presented in which a teacher can act in such an environment: Atelier host (showing the examples of the students), network administrator (connecting people/resources), concierge (showing people where to find stuff), curational (being a knowledge keeper and collector). I believe in the participating student, who is a coach that is playing on the field. I don’t know any examples in real American sports (Baseball, American Football, Ice-Hockey, Basketball) but in soccer sometimes you see a player/coach. A trainer who is also able to play on the field himself. He takes the role of teacher/mentor on and off the pitch. In a blended course that should be the spirit, in my opinion, for a teacher to work in.
The tools that a teacher uses (not mentioned in the readings) seems to be important to. This is a rather personal question. Some like to use whatsapp or a Phone based messaging tool in a “24/7-but-only-if-I-have-the-opportunity-and-energy” agreement with the ‘class’. Others only want to communicatie through mail, fora or other communication tools delivered in a learning environment (chatbox). A teacher should be able to choose the tools in which he/she has intrinsic motivation to use. A team of teachers that is working within the blended course should be aware of each other preferences, but also be configured as a team in a way that different styles are combined. In this way more students can be reached within their own interactional comfort zone.
Interaction between students en between students and teachers delivers engagement. The readings of week 2 conclude with the question how this engagement can be measured. You can imagine Judith that seeing that question made a big smile on my face. I hope that my PhD research can help with that question. Learning Analytics, especially with regard to the learning activities can not only make the engagement, satisfaction and efficiency visible, it perhaps also can change the behavior of the student and the teacher to improve these factors.
But first things first, next step in de BlendKIT course is to work on the Course Documents of my Design Science Research course with interaction on top of my mind and I am going to make a Module Interaction Worksheet.
I will keep you posted.