The Learning + Technology Group (or just LeTech for short) focuses on computing education, educational technology and software visualization. We adopt a research perspective on learning and teaching that allows us to improve education through better educational technologies and teaching methods.Please use the menu on the top to find the theme that you're interested in. If you're looking for publications by LeTech, please see our page on Google Scholar Currently, LeTech has the following active members:
Leader of the research group. Professor Malmi is generally interested in topics related to computing edication research. Some recent particularly interesting questions include: "What is the role of theoretical frameworks in computing education research? How can we better engage students with interactive learning content in computing education? What can we learn from digital traces of student's actions?"
Lauri Malmi received the ACM SIGCSE award for Outstanding contribution to computer science education.
My research interests are on data structures and algorithms, learning analytics, and software visualization. Especially I'm interested in developing applications for online teaching and learning in the context of computer science education. My current work is concerned with software tools and principles in the area of automatic assessment systems. I also act as an instructor and teacher for several courses.
My research interests include: the learning and teaching of introductory programming (especially at the university level), learners' understandings of programming concepts, cognitive approaches to computing education research, program visualization, phenomenography, and learning environments and tools.
For more information, visit Juha's page on Aalto People.
My research interests are: getting more out of automated assessment – finding novel ways of extracting information and using it to construct efficient feedback.
My current research interests lies on how to attract students into CS and motivate in CS with gameful approaches.
My research focuses on investigating computer assisted methods to provide effective formative feedback for learning complex skills, particularly in the context of academic writing in science and technology. My related interests include exploring the similarities between the research on computing education and writing instruction as well as developing tools for interactive online learning.
My current research interests are how to define the cognitive complexity of comprehending computer programs, how to operationalize it using concrete programs and how different plan-composition strategies could affect difficulty in comprehending computer programs. I am also interested in a detailed evaluation of previous programming knowledge using self-evaluation instruments or tests.
My current research explores misconceptions and difficulties that might occur while studying and teaching event-driven programming in university-level introductory programming education.
My research interest is in questions that can change their internal parameters/state based on the students input and thus respond with tuned follow up questions. Due to such questions complexity I am also interested in unit testing at the level of question material authoring and definition of the questions expected behaviour. Naturally, such question materials will generate rich data in the form of the paths students take within the questions and therefore I am interested in visualisation of the distribution of such paths over groups of students and extracting metrics from such paths both with and without the knowledge of the questions structure.
I am currently researching methods to automatically analyse program code that students submit for exercise problems. My aim is to improve automatic feedback from current IO-testing results to include comments and hints when common errors as well as complexity and quality issues are encountered.
In my research, I seek new or better methods of programming teaching. Relying on foundations of Computing Education Research and Cognitive Science, I test hypotheses and impact on learning of each of the following factors: modality effect, segmentation effect, visualisation, learning activities. The teaching base is worked computer programs as examples.
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My research interests revolve mainly around psychosocial constructs and their effects inside learning environments of higher education. Specifically, the sense of belonging of undergraduate students has become my main interest in research studies. Currently, I am working for my doctoral degree and researching computer science students' sense of belonging.
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I am working on a Master's Thesis to improve the Visual Algorithm Simulation Exercises already used in the Data Structures and Algorithms course. The objective of the thesis is to detect typical, systematic mistakes made by students and provide them automatic, corrective feedback utilising machine learning. My research interests also include automatic grading and feedback of graphical programming exercises in the Mathworks Simulink environment.
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