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Technology has helped to innovate in the teaching-learning process. Today's students are more demanding actors when it comes to the environment, they have at their disposal to learn, experiment and develop critical thinking. The area of mathematics has successively suffered from students' learning difficulties, whether due to lack of motivation, low abstraction ability, or lack of new tools for teachers to bring innovation into the classroom and outside it. While it is true that digitalization has entered schools, it often follows a process of digital replication of approaches and materials that were previously only available on physical media. This work focuses on the use of Extended Realities for teaching mathematics, and very particularly in the teaching of geometry, with a proposition of a conceptual model that combines the use of Extended Reality and Machine Learning. The proposed model was subject to prototyping, which is presented as a form of laboratory validation as a contribution to innovate the way in which the geometry teaching-learning process is developed, as well as through the ability to obtain useful insights for teachers and students throughout the process.

In 1949, Captain Alberto Larraguibel and his horse Huaso set the world record for equestrian high jump in Viña del Mar, Chile, by clearing a height of 2.47 meters, a mark that remains unbeaten. This work proposes the use of this historical event as a teaching resource for physics, integrating perspectives from biomechanics and veterinary medicine. Based on the analysis of an audiovisual record of the jump, a kinematic model is developed using the \textit{Tracker} software, determining variables such as displacement, velocity, and acceleration of the horse--rider system. The results make it possible to reflect on the biomechanical and physiological factors involved in animal performance, thus linking physics with real biological processes. It is proposed that this interdisciplinary approach, based on authentic cultural and scientific contexts, may promote meaningful learning, motivation, and a more comprehensive understanding of natural phenomena in science education.

The integration of AI tools into programming education has become increasingly prevalent in recent years, transforming the way programming is taught and learned. This paper provides a review of the state-of-the-art AI tools available for teaching and learning programming, particularly in the context of introductory courses. It highlights the challenges on course design, learning objectives, course delivery and formative and summative assessment, as well as the misuse of such tools by the students. We discuss ways of re-designing an existing course, re-shaping assignments and pedagogy to address the current AI technologies challenges. This example can serve as a guideline for policies for institutions and teachers involved in teaching programming, aiming to maximize the benefits of AI tools while addressing the associated challenges and concerns.

Accessible teaching has been extensively investigated in computer science, yet its integration into other disciplines, such as data literacy, remains limited. This paper examines the potential of data storytelling, defined as the integration of data, visualizations, and narrative, as a possible strategy for making complex information accessible to diverse learners in compliance with Title II of the Americans with Disabilities Act (ADA). We propose six design principles, derived from Title II's core obligations, to guide educators in applying data storytelling within inclusive learning environments. A simulated scenario shows the operationalization of these principles, illustrating how narrative-driven data presentation can enhance comprehension, engagement, and equitable access across different educational contexts.

Paired (or co-)teaching is an arrangement in which two faculty are collaboratively responsible for all aspects of teaching a course. By pairing an instructor experienced in research-based instructional strategies (RBIS) with an instructor with little or no experience in RBIS, paired teaching can be used to promote the adoption of RBIS. Using data from post-course interviews with the novice instructors of four such arrangements, we seek to describe factors that make for effective professional development in teaching via paired teaching. We suggest that the novice instructor's approach to the paired teaching and their previous teaching experience are two aspects which mediate their learning about teaching. Additionally, the structure of the pair-taught course and the sequence of teaching assignments for the novice both likely play roles in lowering the barrier to novice instructors adopting RBIS. We discuss these results within the framework of cognitive apprenticeship.

Machine teaching is an inverse problem of machine learning that aims at steering the student learner towards its target hypothesis, in which the teacher has already known the student's learning parameters. Previous studies on machine teaching focused on balancing the teaching risk and cost to find those best teaching examples deriving the student model. This optimization solver is in general ineffective when the student learner does not disclose any cue of the learning parameters. To supervise such a teaching scenario, this paper presents a distribution matching-based machine teaching strategy. Specifically, this strategy backwardly and iteratively performs the halving operation on the teaching cost to find a desired teaching set. Technically, our strategy can be expressed as a cost-controlled optimization process that finds the optimal teaching examples without further exploring in the parameter distribution of the student learner. Then, given any a limited teaching cost, the training examples will be closed-form. Theoretical analysis and experiment results demonstrate this strategy.

Applying Design Science Research (DSR) methodology is becoming a popular working resource for most Information Systems (IS) and Software engineering studies. The research and/or practical design problems that must be faced aim to answer the question of how to create or investigate an artifact in a given context. Precisely characterizing both artifact and context is essential for effective research development. While various design science guidelines and frameworks have been created by experts in IS engineering, emerging researchers and postgraduate students still find it challenging to apply this research methodology correctly. There is limited literature and materials that guide and support teaching novice researchers about the types of artifacts that can be developed to address a particular problem and decision-making in DSR. To address this gap in DSR, in this chapter, we explore DSR from an educational perspective, explaining both the concept of DSR and an effective method for teaching it. This chapter includes examples of DSR, a teaching methodology, learning objectives, and recommendations. Moreover, we have created a survey artifact intended to gather data on the experiences

Ethnography has become one of the established methods for empirical research on software engineering. Although there is a wide variety of introductory books available, there has been no material targeting software engineering students particularly, until now. In this chapter we provide an introduction to teaching and learning ethnography for faculty teaching ethnography to software engineering graduate students and for the students themselves of such courses. The contents of the chapter focuses on what we think is the core basic knowledge for newbies to ethnography as a research method. We complement the text with proposals for exercises, tips for teaching, and pitfalls that we and our students have experienced. The chapter is designed to support part of a course on empirical software engineering and provides pointers and literature for further reading.

Empirical Software Engineering has received much attention in recent years and became a de-facto standard for scientific practice in Software Engineering. However, while extensive guidelines are nowadays available for designing, conducting, reporting, and reviewing empirical studies, similar attention has not yet been paid to teaching empirical software engineering. Closing this gap is the scope of this edited book. In the following editorial introduction, we, the editors, set the foundation by laying out the larger context of the discipline for a positioning of the remainder of this book.

Mining Software Repositories (MSR) has become a popular research area recently. MSR analyzes different sources of data, such as version control systems, code repositories, defect tracking systems, archived communication, deployment logs, and so on, to uncover interesting and actionable insights from the data for improved software development, maintenance, and evolution. This chapter provides an overview of MSR and how to conduct an MSR study, including setting up a study, formulating research goals and questions, identifying repositories, extracting and cleaning the data, performing data analysis and synthesis, and discussing MSR study limitations. Furthermore, the chapter discusses MSR as part of a mixed method study, how to mine data ethically, and gives an overview of recent trends in MSR as well as reflects on the future. As a teaching aid, the chapter provides tips for educators, exercises for students at all levels, and a list of repositories that can be used as a starting point for an MSR study.

Background: Linear mixed-effects models are central for analyzing longitudinal continuous data, yet many learners meet them as scattered formulas or software output rather than as a coherent workflow. There is a need for a single, reproducible case study that links questions, model building, diagnostics, and interpretation. Methods: We reanalyze a published mouse body-weight experiment with 31 mice in three groups weighed weekly for 12 weeks. After reshaping the data to long format and using profile plots to motivate linear time trends, we fit three random-intercept linear mixed models: a common-slope model, a fully interacted group-by-time model, and a parsimonious model with group-specific intercepts, a shared slope for two groups, and an extra slope for the third. Models are compared using maximum likelihood, AIC, BIC, and likelihood ratio tests, and linear contrasts are used to estimate group differences in weekly means and 12 week gains. Results: The parsimonious model fits as well as the fully interacted model and clearly outperforms the common-slope model, revealing small and similar gains in two groups and much steeper growth in the third, with highly significant contrasts

Feedback is a critical aspect of improvement. Unfortunately, when there is a lot of feedback from multiple sources, it can be difficult to distill the information into actionable insights. Consider student evaluations of teaching (SETs), which are important sources of feedback for educators. They can give instructors insights into what worked during a semester. A collection of SETs can also be useful to administrators as signals for courses or entire programs. However, on a large scale as in high-enrollment courses or administrative records over several years, the volume of SETs can render them difficult to analyze. In this paper, we discuss a novel method for analyzing SETs using natural language processing (NLP) and large language models (LLMs). We demonstrate the method by applying it to a corpus of 5,000 SETs from a large public university. We show that the method can be used to extract, embed, cluster, and summarize the SETs to identify the themes they express. More generally, this work illustrates how to use the combination of NLP techniques and LLMs to generate a codebook for SETs. We conclude by discussing the implications of this method for analyzing SETs and other types o

Due to the corona pandemic, numerous courses were held using digital solutions in order to be able to continue teaching. Conventional collaboration tools (Zoom, Big Blue Button, etc.) were used in particular to digitally map a synchronous session for teaching and learning purposes. While these conventional collaboration tools offer a solid basis for communication between learners and teachers, aspects such as presence or a realistic type of interaction are neglected. In this work, we report on the experiences from a computer science seminar where virtual reality (VR) technology was used as an alternative solution for teaching and group work. The benefits of VR compared to conventional collaboration tools were examined using questionnaires and interviews with the participants. On the one hand, the results show the high potential of VR to increase the clarity and experienceability of learning content and to promote cooperation through social presence. On the other hand, the use of VR brings with it some technical and organizational difficulties that should be taken into account in the didactic implementation.

Machine teaching addresses the problem of finding the best training data that can guide a learning algorithm to a target model with minimal effort. In conventional settings, a teacher provides data that are consistent with the true data distribution. However, for sequential learners which actively choose their queries, such as multi-armed bandits and active learners, the teacher can only provide responses to the learner's queries, not design the full data. In this setting, consistent teachers can be sub-optimal for finite horizons. We formulate this sequential teaching problem, which current techniques in machine teaching do not address, as a Markov decision process, with the dynamics nesting a model of the learner and the actions being the teacher's responses. Furthermore, we address the complementary problem of learning from a teacher that plans: to recognise the teaching intent of the responses, the learner is endowed with a model of the teacher. We test the formulation with multi-armed bandit learners in simulated experiments and a user study. The results show that learning is improved by (i) planning teaching and (ii) the learner having a model of the teacher. The approach giv

Distance teaching has become popular these years because of the COVID-19 epidemic. However, both students and teachers face several challenges in distance teaching, like being easy to distract. We proposed Focus+, a system designed to detect learners' status with the latest AI technology from their web camera to solve such challenges. By doing so, teachers can know students' status, and students can regulate their learning experience. In this research, we will discuss the expected model's design for training and evaluating the AI detection model of Focus+.

In this paper we provide an account of how we ported a text and data mining course online in summer 2020 as a result of the COVID-19 pandemic and how we improved it in a second pilot run. We describe the course, how we adapted it over the two pilot runs and what teaching techniques we used to improve students' learning and community building online. We also provide information on the relentless feedback collected during the course which helped us to adapt our teaching from one session to the next and one pilot to the next. We discuss the lessons learned and promote the use of innovative teaching techniques applied to the digital such as digital badges and pair programming in break-out rooms for teaching Natural Language Processing courses to beginners and students with different backgrounds.

We discuss the development, implementation, and assessment of a course for science undergraduates designed to help them develop an awareness and a deeper appreciation of the intellectual demands of physics teaching. The course focused on increasing student enthusiasm and confidence in teaching by providing well supported teaching opportunities and exposure to physics education research. The course assessment methods include 1) pre/post-tests measures of attitude and expectations about science teaching, 2) self and peer evaluation of student teaching, 3) content-based pre/post-tests given to students who received instruction from the student teachers, and 4) audio-taped focus group discussions in the absence of the instructor and TA to evaluate student perspective on different aspects of the course and its impact.

Amidst the outbreak of the coronavirus (COVID 19) pandemic, distance education, where the learning process is conducted online, has become the norm. Campus-based programs and courses have been redesigned in a timely manner which was a challenge for teachers not used to distance teaching. Students engagement and active participation become an issue; add to that new emerging effects associating with this set-up, such as the so called 'Zoom fatigue', which was coined recently by some authors. In realising this problem, solutions were suggested in the literature to help trigger students engagement and enhance teachers experience in online teaching. This study analyses these effects along with our teachers experience in the new learning environment and concludes by devising some recommendations. To attain the above objectives, we conducted online interviews with six of our teachers, transcribed the content of the videos and then applied the inductive research approach to assess the results.

In this paper we try to organize machine teaching as a coherent set of ideas. Each idea is presented as varying along a dimension. The collection of dimensions then form the problem space of machine teaching, such that existing teaching problems can be characterized in this space. We hope this organization allows us to gain deeper understanding of individual teaching problems, discover connections among them, and identify gaps in the field.