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Technology to support collaborative learning has come a long way. Interactive tabletops support collaboration when correctly integrated in activity design. While these devices can now, in principle, be purchased by the general public, wide adoption in schools is hindered by their high cost and lack of mobility. In this paper, we analyze the potential technologies that could replace such devices. After developing a dozen prototypes to further test the potential of the most promising technologies, we found one robust, affordable and accurate solution: SPART (on-Surface Positioning for Augmented RealiTy) allows augmenting any flat surface (images etc.) with tablets or smartphones, addressing both affordability and mobility. This paper focuses on the technological innovations that were designed for SPART. [For the full proceedings, see ED659933.]

The paper presents an attempt made to use the Arduino platform and the Scratch for Arduino (S4A) environment for the implementation of an educational project for 6th grade primary school students in a classroom attended by refugee, immigrants, and indigenous students. The aim was to investigate the opportunity to use educational robotics as tools to support the social interactions and social inclusion of refugee children in the classroom community. The research was a case study implemented in a primary school in rural Greece. Students had to work in groups to design, build, and program a construction that simulates an "escape room." Students of each group solving a riddle of the other groups must discover a code to unlock the door of the "escape room," based on a series of six worksheets of increasing difficulty to create their final construction. The research tools were a diagnostic questionnaire, an evaluation questionnaire, specially designed worksheets, an observation sheet for participatory observation, and sociometric test tools. Based on the findings it could be supported that educational robotics are able to support social interactions of refugee children to a certain extent. The intervention supported social interactions and collaboration among students but without solving the problem of difficulties in the social integration of refugee students in the classroom community. Verbally all students stated that they had not any problem cooperating with refugee students, but this was not strongly reflected in practice. Greek students interacted strongly with immigrant students and refugee students seemed to prefer interacting with immigrant students too. [For the full proceedings, see ED656038.]

This paper presents results from research that investigated the participation and attrition rate of male and female candidates in two post-secondary mathematics courses (Pure Mathematics and Applied Mathematics). The data for this study consisted of the course results over five years from 2013-2017 for 15,220 candidates (N = 19,585) from 71 post-secondary educational institutions in Jamaica. They were analysed using frequencies (totals and percentages) and descriptive statistics. The data analysis revealed that, on average, over the five years and across each of the five years, approximately 18% of the students who engaged in post-secondary education opted to participate in a mathematics course. However, there was a sharp decrease in the Year 2 student participation after completing the related Year 1 course. Another key finding was that a higher percentage of males chose to undertake Applied Mathematics in Year 2 and equal proportions of male and female candidates in the other three courses. These findings suggest that initiatives are needed which improve student achievement and experience in the first year of each mathematics course which could potentially decrease student attrition across years and curb the leakage at this juncture of the STEM pipeline. [For the complete proceedings, see ED655360.]

As implementation of the "Next Generation Science Standards" moves forward, there is a need for new assessments that can measure students' integrated three-dimensional science learning. The National Research Council has suggested that these assessments be multicomponent tasks that utilize a combination of item formats including constructed-response and multiple-choice. However, little guidance has been provided for determining the relative value or cost effectiveness of those two formats. In this study, students were randomly assigned assessment tasks that contained either a constructed-response or a multiple-choice version of an otherwise equivalent item. Rasch analysis was used to compare the difficulty of these items on the same construct scale. We found that the set of items formed a broad unidimensional scale, but the constructed-response versions were more difficult than their multiple-choice counterparts. This difficulty was found to be partially due to the increased writing demand and the reasoning element in the constructed-response rubric. Students were more likely to recognize a clearly reasoned argument in a multiple-choice item than they were to create that reasoning themselves and communicate it in writing. Our findings can help instrument developers select a set of items that balances the time and effort students must provide during testing and the time and effort scorers need to spend to evaluate and score students' responses. In cases where constructing a response is an essential part of the targeted understanding, as when the target learning goal is to be able to construct an argument or generate a model, CR items are needed, but in other cases, MC items may be more efficient.

Previous research has shown that providing video recommendations to students in virtual learning environments implemented at scale positively affects student achievement. However, it is also critical to evaluate whether the treatment effects are heterogeneous, and whether they depend on contextual variables such as disadvantaged student status and characteristics of the school settings. The current study extends the evaluation of a novel video recommendation system by performing an exploratory search for sources of heterogeneity of treatment effects. This study's design is a multi-site randomized controlled trial with an assignment at the student level across three large and diverse school districts in the southeast United States. The study occurred in Spring 2021, when some students were in regular classrooms and others in online classrooms. The results of the current study replicate positive effects found in a previous field experiment that occurred in Spring 2020, at the onset of the COVID-19 pandemic. Then, causal forests were used to investigate the heterogeneity of treatment effects. This study contributes to the literature on content sequencing systems and recommendation systems by showing how these systems can disproportionally benefit the groups of students who had higher levels of previous algebra ability, followed more recommendations, learned remotely, were Hispanic, and received free or reduced-price lunch, which has implications for the fairness of implementation of educational technology solutions.

Generally, project monitoring and evaluation (M&E) are mandatory actions throughout the project planning, implementation and conclusion phases. Although the terms "monitoring" and "evaluation" go together, evaluation is usually conducted after the project is completed and is typically included as part of the project report, while monitoring is a continuous process of ongoing data collection and feedback. This aspect is often neglected. Although M&E guidelines for practitioners are in abundance, and advocacy for the use of specific models of M&E are aplenty, there is a deficit of scholarly publications on M&E of capacity development projects in higher education in particular. Furthermore, research guiding future M&E endeavours based on lessons learnt before and during the pandemic, is found wanting. The three authors, in different roles, are part of an Erasmus+ co-funded project that aims to build capacity for curriculum transformation through internationalisation and development of Collaborative Online International Learning (COIL). COIL is an inclusive approach to provide all students with virtual internationalised learning experiences without having to travel. Taking an insiders' perspective and sharing our experiences in this project, this paper should inform future M&E of capacity building projects. [For the complete Volume 20 proceedings, see ED622631.]

Augmented Reality (AR) and Virtual Reality (VR) platforms now support shared, immersive experiences that enable people to directly physically and perceptually engage with mathematical objects, including shared objects. These new forms of AR/VR technology -- which we call "shared holographic AR/VR" (shAR) -- enable multiple learners to manipulate and reason about the same mathematical objects represented as holograms projected in a joint three-dimensional collaborative space in front of them, using intuitive hand gestures. This Interactive Tools and Demos paper discusses an AR/VR environment for collaboratively exploring geometry conjectures about shapes and solids. We show data of learners collaborating in our environment using speech, dynamic actions on virtual objects, and hand gestures. We discuss the potential of AR and VR environments for mathematics learning.

This study describes the role that Rasch measurement played in the development of assessments aligned to the "Next Generation Science Standards," tasks that require students to use the three dimensions of science practices, disciplinary core ideas and cross-cutting concepts to make sense of energy-related phenomena. A set of 27 three-dimensional, multi-item tasks were developed and field tested along with a set of multiple-choice test items focused on disciplinary core ideas. Data were collected from elementary, middle, and high school students from across the U.S. Rasch modeling was used to investigate the fit of the data to the model, the dimensionality of the data, differential functioning of the tasks, the relative difficulties of the tasks, and the performance of students by grade band.

The intentional teaching application of leadership theories is not often addressed in leadership development programs comprising the field of adult education. The purpose of this study is to understand more deeply the quality, characteristics, and practices of profound leaders. This is an exploratory, empirical study, interviewing seven participants chosen for leadership acumen and vetted by the research team: employing a two-interview sequence, research apprenticeship model, and thematic analysis. Initial findings include commonly elicited elements, viewed through the lens of integrative literature review findings. Understanding and applying the profound leadership concept offers the field of adult education useful implications with information about leadership development, teaching leadership in higher education and organizations, and practicing leadership allowing flourishing in individuals, organizations, and society. [For the complete volume, "American Association for Adult and Continuing Education Inaugural 2020 Conference Proceedings (Online, October 27-30, 2020)," see ED611534.]

In Technology Enhanced Learning field, learning analytics cover multiple research challenges, among which tracking data analysis and data indicator design and visualization. Part of our research effort is dedicated to changing their design process, in order to capitalize them. This would allow us to meet a need in cost savings of design workflow and to encourage the adoption by users throughout their implications in a simplified design process. A study of the state of the art has been made to explore various solutions which could be exploited in the capitalization of indicators on both levels, design and implementation. It sets a direction toward a user-centered approach, aiming at a better control of users over their observation needs and the use of their data. [For the full proceedings, see ED621108.]

This study examines data from a field experiment investigating the effects of a personalized recommendation algorithm that proposes to students which videos to watch next, after they complete mini-assessments for algebra that available on the Math Nation intelligent virtual learning environment (IVLE). The end users of Math Nation are students enrolled in an Algebra 1 course in middle and high schools of the state of Florida, and the IVLE is used both during and out of school time. The objective of the developed recommendation algorithm is to increase student preparation to take the state-mandated End-of-Course (EoC) Algebra 1 assessment at the end of the school year. The algorithm is based on a Markov Decision Process framework that uses as input the students' responses to a series of mini-assessment tests. The current study randomly assigned 16,406 students to either treatment or control conditions, which were blind to both students and teachers. The results indicate that the effects of the recommendation algorithm depend on the level of usage of students, showing significant improvements on EoC test scores of students who have a moderate level of usage. However, there was no effect for low usage students. The study also shows that students practicing with the mini-assessments available on Math Nation, helps them improve by a small margin their performance on the End-of-Course test, irrespective of the usage level. Finally, the study provides insights on challenges posed for implementing personalized recommendation algorithms at a large scale, related both to student self-regulation and teacher orchestration of technology use in the classroom. [For the full proceedings, see ED615472.]

We address the problem of predicting the correctness of the student's response on the next exam question based on their previous interactions in the course of their learning and evaluation process. We model the student performance as a dynamic problem and compare the two major classes of dynamic neural architectures for its solution, namely the finite-memory Time Delay Neural Networks (TDNN) and the potentially infinite-memory Recurrent Neural Networks (RNN). Since the next response is a function of the knowledge state of the student and this, in turn, is a function of their previous responses and the skills associated with the previous questions, we propose a two-part network architecture. The first part employs a dynamic neural network (either TDNN or RNN) to trace the student knowledge state. The second part applies on top of the dynamic part and it is a multi-layer feed-forward network which completes the classification task of predicting the student response based on our estimate of the student knowledge state. Both input skills and previous responses are encoded using different embeddings. Regarding the skill embeddings we tried two different initialization schemes using (a) random vectors and (b) pretrained vectors matching the textual descriptions of the skills. Our experiments show that the performance of the RNN approach is better compared to the TDNN approach in all datasets that we have used. Also, we show that our RNN architecture outperforms the state-of-the-art models in four out of five datasets. It is worth noting that the TDNN approach also outperforms the state of the art models in four out of five datasets, although it is slightly worse than our proposed RNN approach. Finally, contrary to our expectations, we find that the initialization of skill embeddings using pretrained vectors offers practically no advantage over random initialization. [For the full proceedings, see ED615472.]

We study the usage of a self-guided online tutoring platform called Algebra Nation, which is widely by middle school and high school students who take the End-of-Course Algebra I exam at the end of the school year. This article aims to study how the platform contributes to increasing students' exam scores by examining users' logs over a three year period. The platform under consideration was used by more than 36,000 students in the first year, to nearly 67,000 by the third year, thus enabling us to examine how usage patterns evolved and influenced students' performance at scale. We first identify which Algebra Nation usage factors in conjunction with math overall preparation and socioeconomic factors contribute to the students' exam performance. Subsequently, we investigate the effect of increased teacher familiarity level with the Algebra Nation on students' scores across different grades through mediation analysis. The results show that the indirect effect of teacher's familiarity with the platform through increasing student's usage dosage is more significant in higher grades. [This paper was published in: "The 9th International Learning Analytics & Knowledge Conference (LAK19)" (Tempe, AZ, March 4-8, 2019). New York, NY: Association for Computing Machinery (ACM). (978-1-4503-6256-6).]

Course selection is a crucial and challenging problem that students have to face while navigating through an undergraduate degree program. The decisions they make shape their future in ways that they cannot conceive in advance. Available departmental sample degree plans are not personalized for each student, and personal discussion time with an academic advisor is usually limited. Data-driven methods supporting decision making have gained importance to empower student choices and scale advice to large cohorts. We propose "Scholars Walk," a random-walk-based approach that captures the sequential relationships between the different courses. Based on the "wisdom of the crowd" and the students' prior courses, we recommend a short list of courses for next semester. Our experimental evaluation illustrates that Scholars Walk outperforms other collaborative filtering and popularity-based approaches. At the same time, our framework is very efficient, easily interpretable, while also being able to take into consideration important aspects of the educational domain. [For the full proceedings, see ED599096.]

Grade prediction for future courses not yet taken by students is important as it can help them and their advisers during the process of course selection as well as for designing personalized degree plans and modifying them based on their performance. One of the successful approaches for accurately predicting a student's grades in future courses is Cumulative Knowledge-based Regression Models (CKRM). CKRM learns shallow linear models that predict a student's grades as the similarity between his/her knowledge state and the target course. A student's knowledge state is built by linearly accumulating the learned provided knowledge components of the courses he/she has taken in the past, weighted by his/her grades in them. However, not all the prior courses contribute equally to the target course. In this paper, we propose a novel Neural Attentive Knowledge-based model (NAK) that learns the importance of each historical course in predicting the grade of a target course. Compared to CKRM and other competing approaches, our experiments on a large real-world dataset consisting of ~1.5 grades show the effectiveness of the proposed NAK model in accurately predicting the students' grades. Moreover, the attention weights learned by the model can be helpful in better designing their degree plans. [For the full proceedings, see ED599096.]

The "Next Generation Science Standards" calls for new assessments that measure students' integrated three-dimensional science learning. The National Research Council has suggested that these assessments utilize a combination of item formats including constructed-response and multiple-choice. In this study, students were randomly assigned three-dimensional assessments that contained either constructed-response or multiple-choice versions of items. Rasch analysis was used to compare the difficulty of these items on the same construct scale. We found that partial-credit constructed-response items were located at similar places on the scale as their multiple-choice counterpart but dichotomously-scored constructed-response items were considerably more difficult. This suggests that scoring method influences an item's difficulty, which has implications for what inferences are made from assessments.

More and more domains such as industry, sport, medicine, Human Computer Interaction (HCI) and education analyze user motions to observe human behavior, follow and predict its action, intention and emotion, to interact with computer systems and enhance user experience in Virtual (VR) and Augmented Reality (AR). In the context of human learning of movements, existing software applications and methods rarely use 3D captured motions for pedagogical feedback. This comes from several issues related to the highly complex and dimensional nature of these data, and by the need to correlate this information with the observation needs of the teacher. Such issues could be solved by the use of machine learning techniques, which could provide efficient and complementary feedback in addition to the expert advice, from motion data. The context of the presented work is the improvement of the human learning process of a motion, based on clustering techniques. The main goal is to give advice according to the analysis of clusters representing user profiles during a learning situation. To achieve this purpose, a first step is to work on the separation of the motions into different categories according to a set of well-chosen features. In this way, allowing a better and more accurate analysis of the motion characteristics is expected. An experimentation was conducted with the Bottle Flip Challenge. Human motions were first captured and filtered, in order to compensate for hardware related errors. Descriptors related to speed and acceleration are then computed, and used in two different automatic approaches. The first one tries to separate the motions, using the computed descriptors, and the second one, compares the obtained separation with the ground truth. The results show that, while the obtained partitioning is not relevant to the degree of success of the task, the data are separable using the descriptors. [For the complete proceedings, see ED600498.]

Developing tools to support students and learning in a traditional or online setting is a significant task in today's educational environment. The initial steps towards enabling such technologies using machine learning techniques focused on predicting the student's performance in terms of the achieved grades. The disadvantage of these approaches is that they do not perform as well in predicting poor-performing students. The objective of our work is two-fold. First, in order to overcome this limitation, we explore if poorly performing students can be more accurately predicted by formulating the problem as binary classification. Second, in order to gain insights as to which are the factors that can lead to poor performance, we engineered a number of human-interpretable features that quantify these factors. These features were derived from the students' grades from the University of Minnesota, an undergraduate public institution. Based on these features, we perform a study to identify different student groups of interest, while at the same time, identify their importance. [For the full proceedings, see ED593090.]

This paper analysis the relationship between smartphones and social networks as compatible technologies with the school's pedagogical demands in the 21st century. In the classroom there are some challenges imposed by their own dynamics, these analyses aims to highlight the potential of teaching and learning productivity and the contribution of these technologies focus on the production of knowledge. The information contextualized in this analysis has the focus into the base of the research and the objective and it was collected from a public high school in Palmas, the capital of Tocantins state in Brazil. In this study 212 students and 27 teachers participated. The results confirm the need to change from traditional teaching to practice focused on curricular integration of information and communication technologies (ICT), considering the intense relationship and time of students and teachers with the virtual world. This relationship is intensified by the popularization and dissemination of mobile technologies and the importance attached to social networks in the context of social relations. [For the complete proceedings, see ED590271.]

We compared students' performance on a paper-based test (PBT) and three computer-based tests (CBTs). The three computer-based tests used different test navigation and answer selection features, allowing us to examine how these features affect student performance. The study sample consisted of 9,698 fourth through twelfth grade students from across the U.S. who were randomly assigned to take a test in one of the four modes. CBT modes differed in whether students could skip questions and freely move through the test, and whether students could click directly on the answer choice or had to click on a radio button at the bottom of the screen. Rasch analysis was used to estimate item difficulties and student performance levels. Student performance level was then used as an outcome in hiearchal linear models to determine the mode effects. We found that student performance was unaffected by whether the test was paper-based or computer-based. A comparison of student performance on the three CBTs indicated that restricting test navigation did not affect student performance, but allowing students to select an answer choice by directly clicking on it improved student performance. Our findings show that CBTs can be considered equivalent to PBTs, and the results can also be used to inform best practices for the design of other CBTs.

These Proceedings are a written record of the research presented at the 40th Annual Meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education (PME-NA) held in Greenville, South Carolina, November 15-18, 2018. The theme of the conference is "Looking Back, Looking Ahead: Celebrating 40 Years." The Proceedings include papers from 6 plenary papers, 69 research reports, 133 posters, and 17 working groups. Papers from the research reports, brief research reports, and posters are organized into chapters by topics. Chapters include the following: (1) Plenary Papers; (2) Curriculum and Related Factors; (3) Early Algebra, Algebra, and Number Concepts; (4) Geometry and Measurement; (5) Inservice Teacher Education/Professional Development; (6) Mathematical Knowledge for Teaching; (7) Mathematical Processes; (8) Preservice Teacher Education; (9) Statistics and Probability; (10) Student Learning and Related Factors; (11) Teaching and Classroom Practice; (12) Technology; (13) Theory and Research Methods; and (14) Working Groups. [Individual plenary and research papers are available in ERIC.]

In this study, we investigated how Grade 3 and 4 students' organizational structure for volume units develops through repeated experiences with a virtual manipulative for building prisms. Our data consist of taped clinical interviews within a micro-genetic experiment. We report on student strategy development using a virtual manipulative for counting cubes as a measure of prism volume. A descriptive case of one student, Jim, is included as an example of how students developed increasingly efficient counting strategies built on understanding of structuring, composite units, multiplicative thinking and an understanding of the number of cubes along an edge. We found students were able to develop structure for volume and advance their level of thinking along a learning trajectory for volume measure. [For complete proceedings, see ED581294.]

Can student performance on computer-based tests (CBT) and paper-and-pencil tests (PPT) be considered equivalent measures of student knowledge? States and school districts are grappling with this question, and although studies addressing this question are growing, additional research is needed. We report on the performance of students who took either a PPT or one of two different CBT containing multiple-choice items assessing science ideas. Propensity score matching was used to create equivalent demographic groups for each testing modality, and Rasch modelling was used to describe student performance. Performance was found to vary across testing modalities by grade band, students' primary language, and the specific CBT system used. These results are discussed in terms of the current literature and the differences between the specific PPT and CBT systems.

Energy is a critically important topic in the K-12 science curriculum, with many applications in the earth, physical, and life sciences and in engineering and technology. To meet the challenges associated with teaching energy, new tools and assessment instruments are needed. In this work we describe the development of a three-tier assessment instrument designed for measuring students' understanding of energy at basic, intermediate, and advanced levels. Using a bank of 372 multiple choice items targeting 14 different key energy ideas, three assessment instruments were created to test each of the three levels. These instruments were pilot tested with elementary, middle, and high school students and results were analyzed using Rasch modeling. Our findings show that by using linking items the three instruments form a common scale, allowing items and students to be compared across forms. Each instrument was found to contain items with suitable difficulties for students with a range of understandings of energy. Together these instruments form a reliable tool for measuring the growth of students' understanding of the energy concept as they progress from elementary school to high school.

In recent years, a lot of focus has been given to the study of interactive video. However, interactive video has not been examined as a tool for self-directed learning in the classroom and has not been exploited together with tablets. This study tries to assess the value of an e-learning environment which is based primarily on interactive learning video and which is supposed to enable self-paced learning in the classroom with the use of tablets. A study with 48 undergraduate students attending the third year of their studies in a Pedagogical Faculty was conducted. Students got connected to the online environment and without any guidance, they were asked to follow a learning path concerning thermal heat transfer for 45 minutes. Data collection came through a questionnaire, researchers' observation and focus groups and the participants were asked to express their views both as learners and as future teachers. Undergraduate students were very positive both in regards to the learning efficiency of the environment and the experienced enjoyment. The students assessed the interactive video as interesting, original, unexpected and innovative and were enthusiastic with the new pedagogical approach. Since the new approach requires students to work in teams and each team should be autonomous, they characterized the approach as a truly student-centered and innovative. [For full proceedings, see ED571332.]

Tangible user interfaces (TUIs) are frequently used to teach children abstract concepts, in science and mathematics. TUIs offer a natural and immediate form of interaction that promotes active and hands-on engagement and allows for exploration and reflection. Tangible objects are representational artifacts in their essence, and they increase the representational power, which is a much needed quality in fraction teaching. By exploiting research into the external representations for learning, tangibles and embodied learning, we have designed an interactive tangible number line, named Fractangi, as a conceptual metaphor for helping students to understand and exploit fractions by acting with their hands. A pilot study was conducted in a Department of Primary Education with 65 undergraduate students in order to extract preservice teachers' views on the usability and learning effectiveness of the proposed tangible interface. Preservice teachers' views were extremely positive, in regards both to the context of usage (tangibility, gamification), and to the content of the interaction (learning interactions were considered better than traditional forms of teaching). Fractangi seems able to transform an unpopular learning topic to an enjoyable learning experience. [For full proceedings, see ED571332.]

Energy is a core concept in the teaching of science. Therefore, it is important to know how students' thinking about energy develops so that elementary, middle, and high school students can be appropriately supported in their understanding of energy. This study tests the validity of a proposed theoretical model of students' growth of understanding about energy that moves from a phenomenological understanding, to being able to explain phenomena using basic energy-related concepts, to being able to explain phenomena using more advanced energy-related concepts, often involving atomic/molecular explanations. The study examines results from the administration of 372 distractor-driven, multiple-choice test items aligned to a wide range of energy ideas from energy forms and transformations, to energy transfers, to energy dissipation and degradation, to energy conservation. Over 20,000 students from across the U.S. participated in the study. Rasch modeling provided linear measures of student performance and item difficulty. For most of the 14 targeted energy ideas, an analysis of the item difficulties validated the study's proposed theoretical model of the growth of understanding of the energy concept. Additionally, a cross-sectional analysis of student performance revealed that the high school students outperformed the middle school students and the middle school students outperformed the elementary school students.

Understanding students' misconceptions and how they change is an essential part of supporting students in their science learning. This paper presents results from distractor-driven multiple-choice assessments that target students' misconceptions about energy. Over 20,000 elementary, middle and high school students from across the U.S. participated in the study. Rasch modeling was used to estimate item and student measures. Option probability curves were used to represent the distribution of correct answers and misconceptions across the range of student achievement levels. Analysis of the shapes of the curves and where they occur as a function of student achievement provides insights on changes in students' thinking as they learn more about a topic. These data can then be used to inform instruction.