Search

This paper examines the use of Virtual Reality (VR) at Swiss secondary schools. Despite many years of research, no well-founded data are available on the effects of the technology on children's learning success. It is assumed that VR is compatible with the learning theory of constructivism through the possibility of immersion, interaction, 3D representation and the possibility of adopting several perspectives. To investigate the topic, a vision has been developed for a learning unit within the competence area Nature and Technology on the subject "Plastics and its effects on the environment". Divided into five learning blocks, the learning unit provides the students with knowledge about the structure, production, recycling and environmental consequences with possible approaches to solutions. Ideas for possible VR applications were developed for each of these blocks. The vision was discussed in four interviews with teachers. The VR applications were commented and evaluated by 20 students of the same class. The topic "Environmental Problem Microplastics" selected for implementation is topical, fits into curriculum and is compatible with the specialist areas suitable for VR. In summary, it can be said that VR is still a long way from a nationwide deployment at the Swiss elementary school. Nevertheless, the educational institutions should observe the technology and develop an understanding of its possibilities through pilot tests. [For the complete proceedings, see ED600498.]

This paper describes how beginners of Icelandic as a foreign and second language responded to playing the first scene in Virtual Reykjavik, a video game-like environment where learners interact with virtual characters--Embodied Conversational Agents (ECAs). This game enables learners to practice speaking and listening skills, to learn about the language and culture by solving various tasks. A mixed-method pilot study examined how six learners responded. The results were divided into how learners perceived (1) playing the game in general; (2) interaction with virtual characters; (3) multimodal behaviour of the virtual characters, in particular the speech, facial expressions, hand gestures and body movements; and (4) what the game helped them learn. In conclusion, some learners reported they learned to start speaking in Icelandic and that the spoken behaviour of virtual characters resembled real Icelanders. [For the complete volume of short papers, see ED572005.]

The extensive computer simulation work done in developing the computer adaptive versions of the Graduate Record Examinations (GRE) Board General Test and the College Board Admissions Testing Program (ATP) Scholastic Aptitude Test (SAT) is described in this report. Both the GRE General and SAT computer adaptive tests (CATs), which are fixed length in nature, were developed from pools of items that were calibrated using the three-parameter item response theory model, and item selection was based on the recently developed weighted deviations algorithm (see Swanson and Stocking, 1992), which simultaneously deals with content, statistical, and other constraints in the item selection process. For the GRE General CATs (Verbal, Quantitative, and Analytical), item exposure was controlled by using an extension of an approach originally developed by Sympson and Hetter (1988). For the SAT CATs (Verbal and Mathematical), item exposure was controlled by using a less complex randomization approach. Lengths of the CATs were determined so that CAT reliabilities matched or exceeded comparable full length paper-and-pencil test reliabilities. Eight figures and 23 tables illustrate the analysis. (Contains 21 references.) (Author/SLD)

Nowadays, Augmented and VR in teaching disciplines such as Human Anatomy has made it feasible to promote learning through a gamification process that allows the development of teaching-learning proposals that improve students' motivation significant learning and not merely memoirist. This new practice moves away from the traditional methods used in Human Anatomy subjects. This work shows the result of implementing a methodology based on Augmented and Virtual Reality (VR) in high school students within the subject of human anatomy. Specifically, this implementation was developed in students (n = 16), all of them belonging to a public education center. Moreover, they were involved in nursing care, primary nursing techniques. The results, derived from the analysis of the instrument used to assess their level of acceptance and their level of learning, showed a significant increase in both motivations for learning and the acquisition of knowledge in the area (significance * p <0.001, ANOVA). Our study concludes that the use of Augmented and VR improves the learning and motivation of students in daily practice. [For the full proceedings, see ED623569.]

The worldwide Corona pandemic has severely restricted student projects in the higher semesters of engineering courses. In order not to delay the graduation, a new concept had to be developed for projects under lockdown conditions. Therefore, unused rooms at the university should be digitally recorded in order to develop a new usage concept as laboratory rooms. An inventory of the actual state of the rooms was done first by taking photos and listing up all flaws and peculiarities. After that, a digital site measuring was done with a 360° laser scanner and these recorded scans were linked to a coherent point cloud and transferred to a software for planning technical building services and supporting Building Information Modelling (BIM). In order to better illustrate the difference between the actual and target state, two virtual reality models were created for realistic demonstration. During the project, the students had to go through the entire digital planning phases. Technical specifications had to be complied with, as well as documentation, time planning and cost estimate. This project turned out to be an excellent alternative to on-site practical training under lockdown conditions and increased the students' motivation to deal with complex technical questions. [For the full proceedings, see ED623149.]

Probability is generally considered one of the most challenging areas to teach in mathematics education due to its intricate nature. However, the simulation-based teaching method can increase students' accessibility significantly to the probability problems because it enables students to resolve the problems with minimal mathematical skills. By substantially reducing the stress caused by complex mathematical calculations, simulations help students focus on the key concepts of complex probability problems. Furthermore, the programming languages, such as R and Python, can be easily implemented in classrooms to perform simulations that allow students and teachers to discuss the idea to solve the probability problems step by step. This article presents a classical probability problem known as 'the broken stick problem' to show the efficiency of the simulations in teaching probability. It contrasts the effectiveness of mathematical and computational simulation approaches to the solutions of the broken stick problems with several variations. For concrete illustrations, a series of R codes and their examples are provided at the end of the article, which can be used for the simulation study in probability class with various versions of broken stick problems. The extension of the computational approach to other historic probability problems is also discussed. [For the complete proceedings, see ED626149.]

The essence of Genetics lies in the understanding of the concepts of Central Dogma of Molecular Biology. Although these ideas are fundamental to the field, they are notoriously difficult to understand and visualize. While simulation-based instructional materials are found to improve the teaching-learning process in science education, little has been done to assess their effectiveness in teaching and learning the concepts of Central Dogma of Molecular Biology. Hence, this study aimed to examine the effectiveness of simulation-based instructional materials towards the learning performance of Grade 12 learners on the concepts of Central Dogma of Molecular Biology. This study utilized a mixed-method approach. Results of the pretest and posttest showed that the learners demonstrated from approaching proficiency to advanced mastery level on the concepts of Central Dogma of Molecular Biology. Furthermore, it was revealed that the pretest and posttest mean scores of the learners on the concepts differed significantly (p<0.05). The result of the semi-structured interview revealed that the learners were engaged in experiential learning with the use of simulation-based instructional materials. An action plan was designed based on the study to utilize the simulation-based instructional materials in the teaching-learning process. [For the complete proceedings, see ED626149.]

Simulation-based learning environments have been proposed by researchers, as educational tools to support learning in complex business systems. However, studies have evidenced that subjects may nevertheless have great difficulty understanding and managing dynamic systems. Previous research has revealed positive learning and performance effects of using transparent simulations (that is, revealing users the structure and behaviour of the simulator model). This study explores the effects of combining exploratory guidance, learning goals, and performance goals in a transparent simulation of a dynamically complex system. We present a set of hypotheses about the influence of different goal-setting conditions on participants' performance and comprehension of the system dynamics. In a simulation experiment, participants interacted with a system dynamics model representing the growth of a business venture. Participants who previously worked higher learning goals under exploratory guidance and were then submitted to higher performance goals (compared to vague goals) achieved higher performance and demonstrated better comprehension of the model dynamics. However, participants who were only subjected to more specific, high performance goals (compared to vague performance goals) did not improve their outcomes and revealed larger differences within the treatment group. Two out of the four hypotheses were confirmed. In a transparent simulation of a dynamically complex system, setting specific, high learning goals, previously worked through exploratory guidance, positively moderates the impact of the level of performance goals on the comprehension of the model dynamics and performance. [For the full proceedings, see ED621108.]

Despite the positive effects of mobile augmented reality (MAR)-tools for learning, MAR-tools are not commonly used in classrooms. The scientific discourse identified a lack of concepts that guide the practical application of mobile augmented reality (MAR)-tools in education. Teachers often feel insecure when designing and applying digital learning settings and therefore need tools and concepts to support them. The present contribution outlines how to develop an easy-to-use mobile augmented reality learning setting (MARLS). Thus, a MARLS to foster artificial intelligence (AI)-literacy is developed and studied to explore what makes MARLS easy-to-use. AI-literacy serves as an exemplary topic. It appears to be suiting because MAR-technology is based on AI-systems and hence allows students to experience a positive form of human-AI-interaction first headedly. The educational design research is conducted by means of a conjecture map to enable the concurrent investigation of learning, teaching and its interdependence. Derived from the high-level conjectures, (I): AI-literacy is a set of competences, (II): a digital learning environment is crucial to foster it, (III): a good MARLS considers usability, user centeredness, conscious application, basic learning theories and cognitive load, the MARLS is developed. The MARLS and its conjecture map lay ground for the yet to follow measure and improvement of the learning design. [For the full proceedings, see ED621108.]

These proceedings contain the papers of the 18th International Conference on Cognition and Exploratory Learning in the Digital Age (CELDA 2021), held virtually, due to an exceptional situation caused by the COVID-19 pandemic, from October 13-15, 2021, and organized by the International Association for Development of the Information Society (IADIS). The CELDA conference aims to address the main issues concerned with evolving learning processes and supporting pedagogies and applications in the digital age. There have been advances in both cognitive psychology and computing that have affected the educational arena. The convergence of these two disciplines is increasing at a fast pace and affecting academia and professional practice in many ways. The CELDA 2021 Conference received 68 submissions from more than 21 countries. Out of the papers submitted, 34 were accepted as full papers for an acceptance rate of 50%; 16 were accepted as short papers, and 2 were accepted as reflection papers. In addition to the presentation of full, short and reflection papers, the conference also includes one keynote presentation from an internationally distinguished researcher: Dr. David Scaradozzi, Department of Information Engineering, Università Politecnica delle Marche, Italy. [Individual papers are indexed in ERIC.]

Constructing effective and well-balanced learning groups is important for collaborative learning. Past research explored how group formation policies affect learners' behaviors and performance. With the different classroom contexts, many group formation policies work in theory, yet their feasibility is rarely investigated in authentic class sessions. In the current work, we define "feasibility" as the ratio of students being able to find available partners that satisfy a given group formation policy. Informed by user-centered research in K-12 classrooms, we simulated pairing policies on historical data from an intelligent tutoring system (ITS), a process we refer to as "SimPairing." As part of the process for designing a pairing orchestration tool, this study contributes insights into the feasibility of four dynamic pairing policies, and how the feasibility varies depending on parameters in the pairing policies or different classes. We found that on average, dynamically pairing students based on their in-the-moment wheel-spinning status can pair most struggling students, even with moderate constraints of restricted pairings. In addition, we found there is a trade-off between the required knowledge heterogeneity and policy feasibility. Furthermore, the feasibility of pairing policies can vary across different classes, suggesting a need for customization regarding pairing policies. [For the full proceedings, see ED615472.]

Recent years have seen significant interest in multimodal frameworks for modeling learner engagement in educational settings. Multimodal frameworks hold particular promise for predicting visitor engagement in interactive science museum exhibits. Multimodal models often utilize video data to capture learner behavior, but video cameras are not always feasible, or even desirable, to use in museums. To address this issue while still harnessing the predictive capacities of multimodal models, we investigate adversarial discriminative domain adaptation for generating modality-invariant representations of both unimodal and multimodal data captured from museum visitors as they engage with interactive science museum exhibits. This approach enables the use of pre-trained multimodal visitor engagement models in circumstances where multimodal instrumentation is not available. We evaluate the visitor engagement models in terms of early prediction performance using exhibit interaction and facial expression data captured during visitor interactions with a science museum exhibit for environmental sustainability. Through the use of modality-invariant data representations generated by the adversarial discriminative domain adaptation framework, we find that pre-trained multimodal models achieve competitive predictive performance on interaction-only data compared to models evaluated using complete multimodal data. The multimodal framework outperforms unimodal and non-adapted baseline approaches during early intervals of exhibit interactions as well as entire interaction sequences. [For the full proceedings, see ED615472.]

Knowledge tracing algorithms are embedded in Intelligent Tutoring Systems (ITS) to keep track of students' learning process. While knowledge tracing models have been extensively studied in offline settings, very little work has explored their use in online settings. This is primarily because conducting experiments to evaluate and select knowledge tracing models in classroom settings is expensive. To fill this gap, we introduce a novel way of using machinelearning models to generate simulated students. We conduct experiments using agents generated by the Apprentice Learner Architecture to investigate the online use of different knowledge tracing models (Bayesian Knowledge Tracing, the Streak model, and Deep Knowledge Tracing). An analysis of our simulation results revealed an error in the initial implementation of our Bayesian knowledge tracing model that was not identified in our previous work. Our simulations also revealed a more fundamental limitation of Deep Knowledge Tracing that prevents the model from supporting mastery learning on multi-step problems. Together, these two findings suggest that Apprentice agents provide a practical means of evaluating knowledge tracing models prior to more costly classroom testing. Lastly, our analysis identifies a positive correlation between the Bayesian knowledge tracing parameters estimated from human data and the parameters estimated from simulated learners. This suggests that model parameters might be initialized using simulated data when no human-student data is yet available. [For the full proceedings, see ED615472.]

Cardiopulmonary resuscitation (CPR) is a foundational lifesaving skill for which medical personnel are expected to be proficient. Frequent refresher training is needed to prevent the involved skills from decaying. Regular low-dose, high-frequency training for staff at fixed intervals has proven successful at maintaining CPR competence but does not take into account inherent performance variability across learners. Tailoring refreshers to an individual's past performance would minimize personnel being trained too (in)frequently and would ensure faster knowledge acquisition for new learners. To maximize the benefits of individualized schedules, learning needs gleaned from past training history need to be identified. A recent field study conducted among nursing students showed that a cognitive model-based approach was able to successfully trace the knowledge acquisition and decay of learners and prescriptively devise personalized training regimes that outperformed fixed schedules with regards to both training efficiency and learners' performance. Here, we report a post-hoc analysis of the collected data to investigate whether an alternative modeling approach, blending cognitive modeling and machine learning, could have resulted in even higher quality predictions. Our results reveal modest improvements in predictive accuracy for ensemble models, in which machine learning models predict the prediction errors (i.e., residuals) of the standalone cognitive model. These promising findings reveal strong applied utility for future use in domains where sustained proficiency is a requirement. [For the full proceedings, see ED615472.]

In this UniCollaboration plenary session, Barbara Moser-Mercer speaks on setting up education classrooms in refugee camps with University of Geneva students. She starts off with a video which speaks to the virtual exchange idea and how students from the University of Geneva are working together with students in Macau and how that could be developed and designed and what Kenya means to the students on both ends. At the end of the video, she describes two projects that are ongoing. One of them is geo-mapping snakes and scorpions in the Kakuma Camp (Kenya). The first initiative has already been implemented and landed us a mention in the Global Education Report of UNESCO of which we are very proud. Basically, the students locally designed an information session for parents to help them chart the safest way through the camp to school avoiding the areas with a preponderance of snakes and scorpions. The second project is being followed up with the International Rescue Committee who is responsible for health in the camp. The camp has basic medical training available, however prevention is not funded in the camp. So students designed an education project were they mapped malaria laevae breeding sites. Thus creating a system in the camp to prevent malaria. She ends the session with a question and answer period. [For the complete volume, see ED609298.]

This text presents the results of surveys and interviews of Former Students (FSs) who have taken part in a teacher education course that began in 2004 (still on-going) and that includes Virtual Exchange (VE). The study aimed to look at the impact of two teacher education courses, imparted collaboratively between geographically-distanced universities for over a decade. The course design aims to introduce VE, both theoretically and empirically, as an approach to foreign language teaching in primary and secondary schools. The data are drawn from an online survey as well as in-depth interviews with FSs enrolled in the course between 2004 and 2015. The findings indicate that a significantly high percentage of the FSs who had been exposed to VE had been involved in or intended to implement VE in their own teaching and that the course had provided them with the knowledge and confidence to do so. [For the complete volume, see ED609298.]

Recent years have seen a growing interest in intelligent game-based learning environments featuring virtual agents. A key challenge posed by incorporating virtual agents in game-based learning environments is dynamically determining the dialogue moves they should make in order to best support students' problem solving. This paper presents a data-driven modeling approach that uses a Wizard-of-Oz framework to predict human wizards' dialogue acts based on a sequence of multimodal data streams of student interactions with a game-based learning environment. To effectively deal with multiple, parallel sequential data streams, this paper investigates two sequence-labeling techniques: long short-term memory networks (LSTMs) and conditional random fields. We train predictive models utilizing data corpora collected from two Wizard-of-Oz experiments in which a human wizard played the role of the virtual agent unbeknownst to the student. Empirical results suggest that LSTMs that utilize game trace logs and facial action units achieve the highest predictive accuracy. This work can inform the design of intelligent virtual agents that leverage rich multimodal student interaction data in game-based learning environments.

Engineering virtual internships are simulations where students role play as interns at fictional companies, working to create engineering designs. To improve the scalability of these virtual internships, a reliable automated assessment system for tasks submitted by students is necessary. Therefore, we propose a machine learning approach to automatically assess student generated textual design justifications in two engineering virtual internships, "Nephrotex" and "RescuShell." To this end, we compared two major categories of models: domain expert-driven vs. general text analysis models. The models were coupled with machine learning algorithms and evaluated using 10-fold cross validation. We found no quantitative differences among the two major categories of models, domain expert-driven vs. general text analysis, although there are major qualitative differences as discussed in the paper. [This paper was funded in part by the Office of the Vice Chancellor for Research and Graduate Education at the University of Wisconsin-Madison.] [For the full proceedings, see ED592609.]

Simulations that combine real world components with interactive digital media provide a rich setting for students with the potential to assist knowledge building and understanding of complex physical processes. This paper addresses the problem of modeling the effects of multiple students' simultaneous interactions on the complex and exploratory environments such simulations provide. We work towards assisting educators with the difficult task of interpreting student exploration. We represent the system dynamics that result from student actions with a complex time series and use switch based models to decompose the time series into individual periods that target interpretability for teachers. The model learns the transition points between successive periods in the time series as well as the internal dynamics that govern each period. This model differs from other switch based models in that it decomposes the time series in a way that is human interpretable. This approach was applied to data that was obtained from an existing multi-person simulation with pedagogical goals of teaching sustainability and systems thinking. A visualization of the model was designed to validate the interpretability of the generated text-based descriptions when compared to a movie representation of the system dynamics. A pilot study using this visualization indicates that the switch based model finds relevant boundaries between salient periods of student work. [For the full proceedings, see ED593090.]

This paper attempts to quantify the accuracy limit of "nextitem-correct" prediction by using numerical optimization to estimate the student's probability of getting each question correct given a complete sequence of item responses. This optimization is performed without an explicit parameterized model of student behavior, but with the constraint that a student's likelihood of getting a problem correct only increases or remains unchanged with additional practice (i.e., no forgetting). We present results for this method for the Assistments 2009-2010 data where it suggests that there is only modest opportunity for improvement beyond the state of the art predictors. Furthermore, we describe a framework for applying this method to datasets where problems can be tagged with multiple skills and problem difficulties. Lastly, we discuss the limitations of this method, specifically its inability to give tight bounds on short sequences. [For the full proceedings, see ED593090.]

Student interactions with game-based learning environments produce a wide range of in-game problem-solving sequences. These sequences can be viewed as trajectories through a game's problem-solving space. In this paper, we present a general framework for analyzing students' problem-solving behavior in game-based learning environments by filtering their gameplay action sequences into time series representing trajectories through the game's problem-solving space. This framework was investigated with data from a laboratory study conducted with 68 college students tasked with solving the problem scenario in a game-based learning environment for microbiology education, CRYSTAL ISLAND. Using this representation of student problem solving, we derive the slope of the problem-solving trajectories and lock-step Euclidean distance to an expert problem-solving trajectory. Analyses indicate that the trajectory slope and temporal distance to an expert path are both correlated with students' normalized learning gains, as well as a complementary measure of in-game problem-solving performance. The results suggest that the filtered time series framework for analyzing student problem-solving behavior shows significant promise for assessing the temporal nature of student problem solving during game-based learning. [For the full proceedings, see ED593090.]

Augmented Reality (AR) technology and games can enhance motivation for learning. When combined with mobile devices, AR technology can promote authentic learning in outdoor environments, such as urban parks. These spaces can be used for Science Education, particularly, for environmental education and nature conservation. The EduPARK project combines these elements and follows a design-based research approach to develop an interactive mobile AR game to be explored by students, teachers and the general public, as visitors, in a specific urban park, integrating four interdisciplinary educational guides. The app development involved four cycles of user testing and evaluation for progressive refinement, according to the users' feedback in each cycle. The focus of this paper is to analyze the users' perceptions in the two last cycles of the app refinement, regarding its learning value and usability. The users were students of different school levels (24 of Basic Education; and 46 of Higher Education). Data collection involved a focus group interview, a questionnaire and the app's usage data. Content analysis, descriptive statistics, and System Usability Scale (SUS) computing were conducted. Results revealed that the EduPARK app promotes learning, enjoyment and is easy to use. It achieved an excellent usability, according to younger students (85.8 of average SUS) and a good usability according to older students (70.9). From the last refinement cycle, the app's final version has emerged, which is freely available to the public in the Google Store. In the future, more evaluation experiences are needed to better understand the benefits of this mobile AR game for learning in urban parks. [For the complete proceedings, see ED590271.]