| 1. |
- Afzaal, Muhammad, et al.
(author)
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Informative Feedback and Explainable AI-Based Recommendations to Support Students' Self-regulation
- 2024
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In: Technology, Knowledge and Learning. - 2211-1662 .- 2211-1670. ; 29:1, s. 331-354
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Journal article (peer-reviewed)abstract
- Self-regulated learning is an essential skill that can help students plan, monitor, and reflect on their learning in order to achieve their learning goals. However, in situations where there is a lack of effective feedback and recommendations, it becomes challenging for students to self-regulate their learning. In this paper, we propose an explainable AI-based approach to provide automatic and intelligent feedback and recommendations that can support the self-regulation of students' learning in a data-driven manner, with the aim of improving their performance on their courses. Prior studies have predicted students' performance and have used these predicted outcomes as feedback, without explaining the reasons behind the predictions. Our proposed approach is based on an algorithm that explains the root causes behind a decline in student performance, and generates data-driven recommendations for taking appropriate actions. The proposed approach was implemented in the form of a dashboard to support self-regulation by students on a university course, and was evaluated to determine its effects on the students' academic performance. The results revealed that the dashboard significantly enhanced students' learning achievements and improved their self-regulated learning skills. Furthermore, it was found that the recommendations generated by the proposed approach positively affected students' performance and assisted them in self-regulation
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| 2. |
- Algers, Anne, 1961
(author)
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Open Textbooks: A Balance Between Empowerment and Disruption
- 2020
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In: Technology, Knowledge and Learning. - : Springer Science and Business Media LLC. - 2211-1662 .- 2211-1670. ; 25, s. 569-584
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Journal article (peer-reviewed)abstract
- The use of open textbooks in universities is according to some organisations changing the higher education landscape and is promising for the mainstream adoption of OER. The aim of this paper is to analyse authors’ views of agency and empowerment when they plan, create and reflect on their open textbooks, their teaching and students’ learning. Another aim is to analyse the ways in which knowledge supports authors’ creation of open textbooks and tensions inherent in this practice. This qualitative study used a modified version of a validated questionnaire for 1 h long semi-structured interviews with four interviewees, conducted over 4 days. The data from the interviews were analysed in three steps combining two coding structures, for self-regulated learning and levels of contradictions. The results suggest that the four authors engendered a sense of relational agency in the creation process. They indicated that they valued openness and the pedagogical project itself by highlighting both teachers’ and learners’ perception of agency and empowerment. The data also support the authors’ concerns about the disruptive nature of open textbooks regarding stability versus adaptation and data ownership as it relates to use of learning analytics and commercial interests, indicating considerable contradictions in open textbook practices.
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| 3. |
- Bergdahl, Nina, et al.
(author)
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Covid-19 and Crisis-Promted Distance Education in Sweden
- 2021
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In: Technology, Knowledge and Learning. - : Springer Science and Business Media LLC. - 2211-1662 .- 2211-1670. ; 26, s. 443-459
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Journal article (peer-reviewed)abstract
- This study represents the first research effort to explore the transition from traditional teaching into distance teaching in Swedish schools enforced by covid-19. Governments made gradual and injudicious decisions to impede the spread of the pandemic (covid-19) in 2020. The enactment of new measures affected critical societal functions and included travel restrictions, closing of borders, school closures and lockdowns of entire countries worldwide. Social distancing became the new reality for many, and for many teachers and students, the school closure prompted a rapid transition from traditional to distance education. This study aims to capture the early stages of that transition. We distributed a questionnaire to teachers' (n = 153) to gain insights into teacher and school preparedness, plans to deliver distance education, and teachers' experience when making this transition. Results show that the school preparedness was mainly related to technical aspects, and that teachers lack pedagogical strategies needed in the emerging learning landscape of distance education. Findings reveal four distinct pedagogical activities central for distance education in a crisis, and many challenges faced during the transition. While preparedness to ensure continuity of education was halting, schools and teachers worked with tremendous effort to overcome the challenges. Results expand on previous findings on school closure during virus outbreaks and may in the short-term support teachers and school leaders in making informed decisions during the shift into distance education. The study may also inform the development of preparedness plans for schools, and offers a historical documentation.
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| 4. |
- Fahlgren, Maria, 1966-, et al.
(author)
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A Model for Task Design with Focus on Exploration,Explanation, and Generalization in a Dynamic GeometryEnvironment
- 2014
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In: Technology, Knowledge and Learning. - : Springer. - 2211-1662 .- 2211-1670. ; 19:3, s. 287-315
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Journal article (peer-reviewed)abstract
- The increasing availability of new technologies in schools provides new possibilitiesfor the integration of technology in mathematics education. However, researchhas shown that there is a need for new kinds of task that utilize the affordances provided bynew technology. Numerous studies have demonstrated that dynamic geometry environmentsprovide opportunities for students to engage in mathematical activities such asexploration, conjecturing, explanation, and generalization. This paper presents a model fordesign of tasks that promote these kinds of mathematical activity, especially tasks thatfoster students to make generalizations. This model has been primarily developed to suitthe use of dynamic environments in tackling geometrical locus problems. The model wasinitially constructed in the light of previous literature. This initial model was used to designa concrete example of such a task situation which was tested in action through a case studywith two doctoral students. Findings from this case study were used to guide revision of theinitial model.
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| 5. |
- Hedenqvist, Clarissa, et al.
(author)
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Improving the Learning of Mechanics Through Augmented Reality
- 2021
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In: Technology, Knowledge and Learning. - : Springer Nature. - 2211-1662 .- 2211-1670.
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Journal article (peer-reviewed)abstract
- This study investigates to which extent students’ understanding of the physical phenomenon of torque can be improved through the use of visualization technology, in particular of augmented reality (AR). The students in the first-year course Mechanics I at KTH participated in the study by taking two tests on torque. In between those tests, a subgroup of students participated in a user study where they used an AR application to solve problems regarding torque. The results of the pre-test and the post-test indicate that the subgroup who used the app improved their understanding of torque to a greater extent than the ones who did not use the app. However, a larger sample space would be required to obtain a complete statistical characterization of the reported (qualitative and quantitative) improvement.
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| 6. |
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| 7. |
- Nouri, Jalal, et al.
(author)
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Bachelor thesis analytics to understand and improve quality and performance
- 2020
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In: Technology, Knowledge and Learning. - 2211-1662 .- 2211-1670.
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Journal article (peer-reviewed)abstract
- The bachelor thesis is commonly a necessary last step towards the first graduation in higher education and constitutes a central key to both further studies in higher education and employment that requires higher education degrees. Thus, completion of the thesis is a desirable outcome for individual students, academic institutions and society, and non-completion is a significant cost. Unfortunately, many academic institutions around the world experience that many thesis projects are not completed and that students struggle with the thesis process. This paper addresses this issue with the aim to, on the one hand, identify and explain why thesis projects are completed or not, and on the other hand, to predict non-completion and completion of thesis projects using machine learning algorithms. The sample for this study consisted of bachelor students’ thesis projects (n=2436) that have been started between 2010 and 2017. Data were extracted from two different data systems used to record data about thesis projects. From these systems, thesis project data were collected including variables related to both students and supervisors. Traditional statistical analysis (correlation tests, t-tests and factor analysis) was conducted in order to identify factors that influence non-completion and completion of thesis projects and several machine learning algorithms were applied in order to create a model that predicts completion and non-completion. When taking all the analysis mentioned above into account, it can be concluded with confidence that supervisors’ ability and experience play a significant role in determining the success of thesis projects, which, on the one hand, corroborates previous research.On the other hand, this study extends previous research by pointing out additional specific factors, such as the time supervisors take to complete thesis projects and the ratio of previously unfinished thesis projects. It can also be concluded that the academic title of the supervisor, which was one of the variables studied, did not constitute a factor for completing thesis projects. One of the more novel contributions of this study stems from the application of machine learning algorithms that were used in order to – reasonably accurately – predict thesis completion/non-completion. Such predictive models offer the opportunity to support a more optimal matching of students and supervisors.
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| 8. |
- Nouri, Jalal
(author)
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Students Multimodal Literacy and Design of Learning During Self-Studies in Higher Education
- 2019
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In: Technology, Knowledge and Learning. - : Springer Science and Business Media LLC. - 2211-1662 .- 2211-1670. ; 24:4, s. 683-698
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Journal article (peer-reviewed)abstract
- Information and communication technologies have increasingly been integrated in our everyday lives, and as many would say changed how we acquire knowledge and how we learn. It is against such a background this paper will describe how higher education students engage with technology during self-studies and how they in particular utilize different semiotic affordances of information and communication technologies in order to learn course content. Consequently, focus is put on how university students design their learning during self-studies through exploiting multimodal literacy and by constructing knowledge through different modes and media. The paper reports on a mixed-method study and presents findings that points to that (1) students are becoming active designers of learning due to access to new modes and media that can be tailored to their needs, (2) that students have developed a multimodal digital literacy to various degrees, and (3) that students are provided opportunities for enhanced and more effective learning than before because of the availability of affordances of contemporary technology. Thus the paper calls for a pedagogical shift that take departure from a design-oriented, multimodal understanding of learning.
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| 9. |
- Olsson, Jan, et al.
(author)
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Dynamic software, task solving with or without guidelines, and learning outcomes
- 2019
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In: Technology, Knowledge and Learning. - : Springer. - 2211-1662 .- 2211-1670. ; 24:3, s. 419-436
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Journal article (peer-reviewed)abstract
- The present study contributes to knowledge about how to design tasks that benefit from dynamic software in math education, comparing practice performance and learning outcomes among 129 students practicing on two different task designs using GeoGebra. The task designs differed with respect to the presence or absence of guidelines on how to solve the task. One student group practiced on the guided task while the other student group practiced on the unguided task, and 1 week later a posttest was conducted. Data were statistically analyzed and showed significant differences with regard to success during practice for students solving the guided task. Among the students who succeeded in solving the task (guided or unguided) during practice, however, the analysis showed significant differences in the posttest performance in favor of the unguided students.
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| 10. |
- Qushem, Umar Bin, et al.
(author)
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Unleashing the Power of Predictive Analytics to Identify At-Risk Students in Computer Science
- 2023
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In: Technology, Knowledge and Learning. - : Springer. - 2211-1662 .- 2211-1670.
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Journal article (peer-reviewed)abstract
- Predicting academic performance for students majoring in computer science has long been a significant field of research in computing education. Previous studies described that accurate prediction of students’ early-stage performance could identify low-performing students and take corrective action to improve performance. Besides, adopting machine learning algorithms with predictive analytics has proven possible and meaningful. The traditional approach of looking after students without uncovering the root causes of poor performance has shifted dramatically into improving the quality of the educational processes of students, teachers, and stakeholders. Thus, this study employed predictive analytics to develop an early warning prediction model using computing science degree performance data at a public institution. Predictive models based on our data analysis revealed that low, medium, and high-performing students could be predicted with an accuracy of 88% using only the grades of the courses they took in the second year. Moreover, 96% accuracy was achieved when all course grades were used in predictive models. The courses that are important in determining the overall performance of the students were also analyzed. By employing a multi-method approach, utilizing a large dataset spanning four academic years, and including a diverse sample of 430 students, our study offers a robust foundation to researchers, designers, and computer science educators for understanding and predicting student performance. The enhanced generalizability and implications for educational practice position our study as a valuable contribution to the field, paving the way for further advancements in predictive analytics.
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