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We do not have any specific measures from previous years.However, we improved the group formation process including a group requirements form and a dedicated session for helping students to find their groupmates.Moreover, we refined/updated some of the learning materials.

The assessment of the course consists of 3 mandatory assignments and a semester project. The students managed to deliver the needed assignments and the semester project was of good quality. Overall, both the teacher and the students (based on the reference group meetings, discussions in the classroom and feedback via an online evaluation form) were happy with the outcome and the final deliverables. From the final group reports, it is clear that the students achieved the expected mastery of the subject. This is also the feedback from the supervisors who have MSc theses in learning technologies (e.g., Papavlasopoulou, Sharma), there is a common agreement that this course achieved the expected LO and helped students (especially the 5th-year ones) to prepare the work of their master thesis.

The overall feedback from students and my personal experience are very positive, some practical aspects can be improved (I list them below). From the feedback received, we need to communicate the goal of the project a bit earlier. Although the course is relatively small and most of the students participate in the classes, it is important to communicate the expectations of the final projects earlier so the teams can start working on the project early. This is particularly important for some students who do not participate in all the lectures and miss some discussions. The feedback has been collected via: 1) face-to-face interaction between the teacher and the students during the lecture, 2) emails and online communication with students, 3) via the reference group members and meetings, and 4) online feedback form (ca. 50% response rate). In general, the semester went as planned, with good quality project reports and good learning experience from students (based on the feedback form and the reference group discussion).

NTNU:TIA:REST

Institutt for datateknologi og informatikk

Department of Computer Science

Alle trykte og skrevne

Engelsk

Mer om vurdering

Rapporten fra prosjektet er grunnlaget for karakteren i kurset. Studentene må også levere obligatoriske leveranser gjennom hele semesteret.Karakterisering er teambasert, men individuelle karakterer kan gis i spesielle tilfeller.Ved gjentak må hele emnet gjentas.

Anbefalte forkunnskaper

IT3402 Design av grafiske brukergrensesnitt (eller kontankt faglærer for evaluering).

Forkunnskapskrav

Emnet er forbeholdt studenter opptatt på MSIT-retningen "Interaksjonsdesign spill- og læringsteknologi", MTDT/MIDT-profilene "Programvareutvikling", "Digital virksomhetsutvikling", eller "Interaksjonsdesign og spillteknologi" og lektorutdanning i realfag (studieretningen for matematikk og informatikk) (eller kontankt faglærer for evaluering).

Faglig innhold

Emnet er en kombinasjon av forelesninger og prosjektarbeid. Forelesningene vil støtte fremdriften i prosjektet, prosjektet har forskjellige leveranser og gjøres i team. Hvert team må jobbe med et læringssystem, implementere det, samle empiri og analysere dem. Gruppevis endelig presentasjon av systemet og den innsamlede læringsanalysen. Studentene må sende inn grouppeleveranser (obligatorisk) og gruppeprosjektet (som teller 100% av den endelige evalueringen) for å bestå emnet.

Kursmateriell

Læringsutbytte

Emnet gir grunnleggende kunnskaper om læringssystemer og læringsanalyse. Studentene vil etter endt kurs kunne:- Forstå utformingen og utviklingen av moderne læringssystemer.- Forklar hvordan en teknologifunksjon påvirker læringsprosesser.- Evaluer effekten av et læringssystem gjennom elevgenererte data (dvs. læringsanalyse).- Utfør en studie som vurderer effektiviteten og effektiviteten til en læringsteknologi gitt målgruppen og forventede læringsresultater.

Læringsformer og aktiviteter

Forelesninger og prosjektarbeid. Hvert team må levere prosjektleveranser i løpet av semesteret og en sluttrapport. Emnet undervises på engelsk.

Deliverables

Læringsteknologi og analyse

Informatikk

Informasjonsteknologi

Tekniske fag

Datateknologi - masterstudium (5-årig)

Datateknologi - masterstudium (2-årig)

Lektorutdanning i realfag for trinn 8 – 13

Informatikk - masterstudium

Master of Science in Informatics

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First meetingThe learning environment:  Most of the courses are kind of clear. There are problems to use Blackboard but more for their blackboards. All the needed information can be found.  Open lecture, you can ask questions anytime you take the breaks.  open environment where I can ask questions and I don't feel like dumb. The room was a perfect choice because that way we can just sit in groups and even after the lectures. for next semester or the upcoming time to maybe book it one hour more than the lectures so some people can stay there and work on in the groups information provided on Blackboard, I think it's good, especially the template provided in the first assignment.  Lectures and Assignments The explanations for the assignment was clear; no need to guess what is to be done;  latex template had some issues; some minor issues that some stuff doesn't work, but we were able to fix it. Not many problems in the lectures. Sometimes the lectures do not fill up the two complete hours. Suggestion: add group work and discussions to such lectures.  Second MeetingThe learning environment:  The learning employment in the course itself is for my opinion, and what I hear from my other students quite good. You take room for the questions. You have time after the lecture when you have, when you have an individual question, you take the time you sit to the students and answer that question. And that's quite good. The blackboard itself is also after I get used to it a bit for me, pretty clear structured. I was scrolling preparation of this meeting, but through all presentations from the lectures for some self reflection there are no issue to find something. A new structure on Blackboard, it's pretty easy to navigate. Also through this course it's pretty transparent. You know where you can find things, they're datasets, you know where everything is.   Lectures and Assignments What we maybe missed a bit, but they're really minor things and they're just out of my own reflection when we're talking about human computer interaction. And we were talking a lot about child computer interaction. But we didn't talk about how we can verify a system with child's how that works as an example, as an SUS works with with a child, can I hand out an SOS to a child? And does that work?  The course was really theoretical. Also regarding the dashboard, we have to provide a prototype, but I was thinking when I read the course description that we are going to do something hands on with the specific learning technology. The teaching style works and that's good and you can go on with that. Students don't have any complaints with you. They also always say that you're pretty transparent in feedbacks and everything and you're always available.   Suggestion: Some lecture are dense with the information add hands on activities to such lectures.

First meetingCommunication:   Sharma sent an announcement out to the students about reaching out to the reference group because not everybody is present in the class usually. So Sharma send them an e-mail that they can reach out to Kißmehl and Irfan if they have any feedback. The reference group did not get any individual feedback via emails.  Information flow between students and the teaching staff: good practical examples and attempt to integrate students and are always really helpful. Interesting course, a good introduction to learning technologies with AI and ML. More on organization and introduction in the beginning and a bit late to the main content.  The data science part is interesting but lot of new things.  Suggestions for the future: More hands-on experience: working with some learning technologies; to train some learning technologies and see what the outputs are; not only writing reports and not only writing assignments;  Second MeetingCommunication:   Ohh well for me the information flows quite clear. Didn't get any better comments on this one. There is clear communication from you, you usually send emails out. If something happens, or actually you remind us when, as an example no lecture. And that's actually quite nice also the Uploads are from the lectures. I guess they're almost instantly, so this is from outside. Quite good to say.  Information flow between students and the teaching staff: the feedback is pretty clear and everything and for example we got the feedback regarding the second assignment. That's something with analysis was missing and you were very transparent about it and yeah. So I think there is like there is no issues between communication. The students got it what we did right and what is missing. The feedback is pretty good. You point out weaknesses in the assignments. Then I can think about myself and what I had did wrong or did not have wrong. I don't know. And this is a pretty outstanding what do you do that it takes so much time for all the assignments and write really feedback individually. Suggestions for the future: More hands-on experience: working with some learning technologies; to train some learning technologies and see what the outputs are; not only writing reports and not only writing assignments;

Rapporten fra prosjektet er grunnlaget for karakteren i kurset. Studentene må også levere obligatoriske leveranser gjennom hele semesteret. Ved gjentak må hele emnet gjentas.

Forelesninger og prosjektarbeid. Hvert team må levere prosjektleveranser i løpet av semesteret og en sluttrapport. Karakterisering er teambasert, men individuelle karakterer kan gis i spesielle tilfeller. Emnet undervises på engelsk.

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This is the first time that this course takes place. It is a new course created to support students' taking their MSc thesis in learning technologies and/or analytics related topics.

The course gives fundamental knowledge of learning systems and learning analytics. In particular, on completion of the course, students will be able to: - Understand the design and development of contemporary learning systems. - Explain how a technology feature influences learning processes. - Evaluate the effect of a learning system through learner-generated data (i.e., learning analytics). - Elaborate a study that assesses the effectiveness and efficiency of a learning technology given the target audience and expected learning outcomes.  The assessment of the course consists of 3 mandatory assignments and a semester project. The students' managed to deliver the needed assignments and the semester project was of high quality. Overall, both the teacher and the students (based on the reference group meetings and feedback) were happy with the outcome and from the final deliverables, it is evident that the students' achieved the expected mastery of the subject. This is also the feedback from the supervisors who are having MSc theses in learning technologies (e.g., Papavlasopoulou, Sharma), there is a common agreement that this course achieved the expected LO.

The overall feedback from students and my personal experience are very positive, there are some practical aspects that can be improved (I list them below), but this is okay for a course that is taught for first time. From the feedback received, we need to do some minor refinements to the learning materials (e.g., update the ebook, materials for the 1st assignment) and add supportive sessions for the team formation. The course is relatively small and most of the students participate in the classes. The feedback has been collected via: 1) face-to-face interaction between the teacher and the students during the lecture, 2) emails and online communication with students and 3) via the reference group members and meetings.  Due to COVID, we had to do some small adjustments (e.g., hybrid lecturing) and some notes have been kept for next the next year (e.g., to develop some additional materials for the first assignment, and help more with the teams formation). But other than those common (due to COVID) adjustments, the semester went as planned.

Rapporten fra prosjektet er grunnlaget for karakteren i kurset. Studentene må også levere obligatoriske leveranser gjennom hele semesteret. Ved gjentak må hele emnet gjentas. 

Emnet er forbeholdt studenter opptatt på MIT-retningen "Interaksjonsdesign spill- og læringsteknologi", MTDT/MIDT-profilene "Programvareutvikling", "Digital virksomhetsutvikling", eller "Interaksjonsdesign og spillteknologi" og lektorutdanning i realfag (studieretningen for matematikk og informatikk).

Emnet er en kombinasjon av forelesninger og prosjektarbeid. Forelesningene vil støtte fremdriften i prosjektet, prosjektet har forskjellige leveranser og gjøres i team. Hvert team må jobbe med et læringssystem, implementere det, samle empiri og analysere dem. Gruppevis endelig presentasjon av systemet og den innsamlede læringsanalysen. Studentene må sende inn individuelle leveranser (obligatorisk) og gruppeprosjektet (som teller 100% av den endelige evalueringen) for å bestå emnet.

Forelesninger og prosjektarbeid. Hvert team må levere prosjektleveranser i løpet av semesteret og en sluttrapport. Karakterisering er teambasert, men individuelle karakterer kan gis i spesielle tilfeller. Emnet undervises på engelsk. 

Emnet gir grunnleggende kunnskaper om læringssystemer og læringsanalyse. Studentene vil etter endt kurs kunne: - Forstå utformingen og utviklingen av moderne læringssystemer. - Forklar hvordan en teknologifunksjon påvirker læringsprosesser. - Evaluer effekten av et læringssystem gjennom elevgenererte data (dvs. læringsanalyse). - Utfør en studie som vurderer effektiviteten og effektiviteten til en læringsteknologi gitt målgruppen og forventede læringsresultater. 

Lektorutdanning i realfag - masterstudium (5-årig)

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Læringsteknologi og analyse - IT3023