Educational Robotics (ER) is a Held of study that involves the integration of robotics into educational settings to enhance the learning experience. The primary goal is to engage students in hands-on, interactive activities that promote problem-solving, creativity, collaboration, and critical thinking. This study delves into the effectiveness of scaffolds to foster problem-solving through robotics activities, focusing on undergraduate engineering students. Through detailed analysis of participant activities, artifacts, and semi-structured interviews, the research addresses questions regarding scaffold effectiveness and student perceptions of a robotics learning platform. Key findings reveal challenges related to limited prior knowledge, team dynamics, and motivation. The study highlights the importance of clear instructions, well-defined guidelines, and motivation-related resources to enhance engagement and problem-solving skills.

It has become increasingly important to understand the challenges in IoE-based engineering education due to stress among the academicians who are forced to work remotely due to the lockdown imposed as part of stringent COVID-19 protocol. In this study, an investigation is carried out to understand the nature of stress and its impact on a cohort chosen from academics spread across India. A semi-structured survey investigates the nexus between work & stress among young academicians- how it varies as per demographic, neighborhood, entertainment, lectures, and performance. The study shows that the post-graduate participants and those aged 26–30 years are 14-15% more stressed. Those who are working-from-home in rural areas are more stressed. Participants with the highest levels of stress are found to underrate their own performance as well as others' involvement. It is observed that spending more time on entertainment or developing new interests may reduce stress levels. The study also revealed that carefully designed lectures might lower stress levels as most participants realized. The study contributes to the knowledge base for considering stress associated factors in learning activity.

Online learning and MOOCs have become increasingly popular in recent years, and the trend will continue, given the technology boom. There is a dire need to observe learners' behavior in these online courses, similar to what instructors do in a face-to-face classroom. Learners’ strategies and activities become crucial to understanding their behavior. One major challenge in online courses is predicting and preventing dropout behavior. While several studies have tried to perform such analysis, there is still a shortage of studies that employ different data streams to understand and predict the drop rates. Moreover, studies rarely use a fully online team-based collaborative environment as their context. Thus, the current study employs an online longitudinal problem-based learning (PBL) collaborative robotics competition as the testbed. Through methodological triangulation, the study aims to predict dropout behavior via the contributions of Discourse discussion forum ‘activities’ of participating teams, along with a self-reported Online Learning Strategies Questionnaire (OSLQ). The study also uses Qualitative interviews to enhance the ground truth and results. The OSLQ data is collected from more than 4000 participants. Furthermore, the study seeks to establish the reliability of OSLQ to advance research within online environments. Various Machine Learning algorithms are applied to analyze the data. The findings demonstrate the reliability of OSLQ with our substantial sample size and reveal promising results for predicting the dropout rate in online competition. Overall, the study contributes to online learning by addressing the need to understand and predict dropout behavior in online courses. The study’s methodological triangulation, involving qualitative interviews, provides insights into such contexts' unique dynamics and challenges by utilizing a fully online team-based collaborative environment.

Given the lockdown situation due to the global pandemic in place, teaching and learning hardware-oriented hand-son skills became a major challenge for engineering education. The present study investigates some of the approaches and demonstrates an effective method of teaching modern engineering skills in a simulated environment, such as investigating the Industry 4.0 concept of ‘Warehouse Automation.’ As part of Project-Based Learning, we are using open source software and free services in a six-month online robotics competition with several stages and tasks. This learning activity was implemented among 1880 participants (470 teams) to teach complex engineering concepts from multidisciplinary domains such as - (1) Robot Operating System (ROS) to control two Robotic Arms in a dynamic simulator, (2) Internet of Things protocols such as MQTT and HTTP, and (3) free cloud services to log data in a database and developing an email notifications system. All the necessary resources were provided to the participants along with the troubleshooting guide that was provided via an online discussion forum. After each task performance of teams was recorded and feedback was collected from the participants. All the recorded data was passed through various statistical analyses as a part of the study to assess the effectiveness of this teaching and learning activity. The study further inspects- whether there is a correlation between participants’ performance in the academic-curricular and the competition, and whether more interaction through online-discussion-forum leads to better performance. Finally, the study will help us understand the perception of participants about COVID-19 impact on their performance.

A Learning Analytics Dashboard is a quick and efficient way for instructors to track the activities of students. In massive online learning scenarios like an international robotics competition, a dashboard is a critical tool for instructors to ensure continuous engagement of participants. Previous research on learning analytics dashboards focused on the effectiveness of dashboards and learning analytics on students along with factors affecting its success. This research discusses a dashboard developed for a massive robotics competition through which each year thousands of students are trained in engineering skills in an online Project Based Learning approach. The dashboard is developed using the dataset for the competition conducted during September 2020 to April 2021 in which more than 10,000 undergraduate students from 572 academic institutions across 7 countries participated. Team characteristics like demographics, feedback, scores, online activity, etc. are considered to cluster teams and develop models to predict the retention of participants. The Machine Learning (ML) model was able to achieve an accuracy of 80.7% and a recall value of 83.9% to identify dropping teams. Clustering provided insights on how these characteristics affected the performance of participants. These predictions along with participant engagement and feedback data was displayed on the dashboard. This visualization helps instructors identify teams requiring guidance or scaffolds to continue participation. Feedback from instructors shows the dashboard to be a promising tool for effectively managing massive online competitions.

Despite its difficulties, SQL is an essential tool for the users in an educational organisation who need quick and easy access to data to gauge the reception of their learning content by their students and potentially improve their content depending on the insights. To get these insights, the course instructors need real-time access to the database and also need to have relevant SQL knowledge to operate the database to retrieve the required data. The study explores ways to mitigate the difficulties of SQL by developing an application that takes natural language questions that the course instructors have and convert them into SQL queries using a sequence-to-sequence model that show them the data they asked for on a dashboard. The study found that there was a drastic reduction in the time it took for the users of the e-learning platform to get the data from the database without waiting for support from the database administrators. This in turn empowered the educators to study the data and get insights into the reception and working of the course and make suitable changes if necessary which might enhance the user experience for their students.

MOOCs are experiencing high enrolment statistics and academics are focusing on ways to improve the retention rates in MOOCs. e-Yantra imparts hands-on engineering skills in a highly scalable manner to undergraduate students in engineering, polytechnic and science colleges. The e-Yantra MOOC takes form of a Robotics Competition where students are mentored to solve problems modelled as “Themes.” We study the effect of “difficulty levels” on participant retention of two Themes (hands-on MOOCs). The performance of 800+ students that participated in each of these MOOCs (1600+ total students) offered in 2017 and 2018 is considered. In the light of increasing popularity of online learning especially through MOOCs, this study contributes to the literature of “funnel of participation” in MOOCs teaching conceptually difficult engineering topics in a hands-on Project Based Learning mode. Our study brings contrast with the general comprehension from prior literature that shows MOOCs teaching easier concepts to have high completion rates and MOOCs teaching difficult concepts to have low completion rates. We find simplicity and coherence of MOOC design, regardless of difficulty level of MOOC to have a stronger effect on learning outcomes. Conceptually difficult MOOC in our study has “2.75” times higher completion rate compared to the easier MOOC. This affirms that it is better to teach solving “one challenging problem” than to teach solving a series of easy problems since the latter leads to “conceptual clutter” among participants and as a result a fatigue and consequential increased dropouts.

As the hands-on engineering education got severely affected by the lockdown due to the COVID-19 pandemic, the present study discusses how to adopt the Project-Based Learning (PBL) approach to teach complex engineering concepts in this tough time. In this paper, we have discussed the design of a gamified problem statement (using a robotic simulation environment called CoppeliaSim) which was used to teach complex engineering concepts like image processing, control systems, path planning, etc to undergraduate students by a pioneering initiative in engineering education. The study was implemented on 469 teams (1876 students) and explores how the use of a simulation environment impacts the overall performance of teams in completing the assigned problem statement. In addition to this, we have demonstrated the use of a leaderboard to increase learner engagement and motivation in completing the problem statement. Our work is useful to anyone seeking to use PBL to teach and/or learn complex engineering concepts.

e-Yantra is an initiative by IIT Bombay that uses a Project Based Learning (PBL) approach to solve real world problems. e-Yantra conducts an annual robotics competition for students in polytechnic, science, and engineering colleges where robotic concepts and emerging technologies are taught through “themes”. Each year features 6-7 themes where a theme is targeted towards distinct skill sets in participants. Registration has increased from 4500 students (2012) to 34500 students (2019). Goal is to teach complex engineering skills through an online competition in a scalable manner using a paradigm that “gamifies” learning. A theme is developed and tested in-house before it is made available to teams. Students register as a team (group of four students) and take an online selection test to participate in e-Yantra Robotics Competition (eYRC) that runs for around six months. 4200 students took part in a “Rapid Rescuer” theme (one of six themes) that included elements such as Image Processing, Algorithm building, Wireless Communication, Building a robot, Embedded-C programming and much more. We describe here the tasks given in the competition and the use of auto-evaluation to build scale. Open Source COTS components make the competition accessible to even remote or “resource-challenged” participants. We present the impact of Project Based Learning (PBL) approach on learning imparted through competition.

Recursive neural networks (RvNN) have been shown useful for learning sentence representations and helped achieve competitive performance on several natural language inference tasks. However, recent RvNN-based models fail to learn simple grammar and meaningful semantics in their intermediate tree representation. In this work, we propose an attention mechanism over Tree-LSTMs to learn more meaningful and explainable parse tree structures. We also demonstrate the superior performance of our proposed model on natural language inference, semantic relatedness, and sentiment analysis tasks and compare them with other state-of-the-art RvNN based methods. Further, we present a detailed qualitative and quantitative analysis of the learned parse trees and show that the discovered linguistic structures are more explainable, semantically meaningful, and grammatically correct than recent approaches. The source code of the paper is available here.

The e-Yantra project at premier technical institute in India – Indian Institute of Technology Bombay, conducts a massive Online Robotics Competition known as e-Yantra Robotics Competition annually, with the objective to develop ground-breaking digital pedagogy to indulge students in the domain of Robotics and Embedded Systems through hands-on practical medium of Project Based Learning (PBL). The students work in collaboration as a team of four from varying years and disciplines of engineering and implement a 'Theme' - a gamified robotics problem statement over the course of six months. Previous studies of e-Yantra competition have shown that the competition has been successful in teaching students new and high-level concepts of embedded systems and robotics in effective manner through a learning by competing approach. Over the course of various editions of the competitions, e-Yantra has collected a huge corpus of participants and their respective performances in the competition. This paper analyzes and discusses the outcomes of the data collected over a three-year period (2016-2018) and draws inferences from it. It successfully demonstrates that students in an online Project Based Learning environment receive a functional upgrade in the knowledge of robotics. While most project based learning studies have generally been restricted to a rather small sample size, this is one of the few quantitative study that deals with a large number of students who are not in a classroom environment. This approach also facilitates cooperative and collaborative learning in addition, it further encourages and develops intra-personal and inter-personal skills. A key recommendation of the study is to make Project Based Learning competitions like e-Yantra to be more ingrained in the college curriculum. Such competitions can be introduced in universities with incentives such as extra credits and certifications and hence will enable active participation of students in the engineering studies.

e-Yantra is a robotics outreach project funded by MHRD, Govt of India, and hosted at IIT Bombay. It organizes e-Yantra Robotics Competition (eYRC), an annual robotics competition that teaches robotics concepts scalably to college students. Last year 34500+ students registered in the competition out of which 800 students participated in a theme called “Thirsty Crow” which taught augmented reality concepts. In this paper, we illustrate how “project-based learning” may be used to teach complex skills in a “game-like” context. We demonstrate how we designed, experimented and implemented the concepts of “Marker Based Augmented Reality” using open source technologies such as OpenCV, OpenGL, and Blender. We demonstrate in this paper that the average completion rate of our theme (which is essentially a hardware-based MOOC) is 11% which is greater than the overall average completion rate of all themes in eYRC-2018 (which is 6.2%). Our work is useful to anyone wishing to incorporate augmented reality in teaching courses, generally in the area of computer science.

Embedded systems and Robotics are subjects that involve multi-disciplinary approaches to problem solving with an emphasis on hands-on experiments. Due to lack of infrastructure — robotics labs to execute projects, or trained teachers to mentor projects — engineering students in India do not get the benefits of hands-on experience. e-Yantra Lab Setup Initiative is designed as a scalable and sustainable approach that addresses infrastructure creation and teacher training to create an ecosystem at colleges to impart effective engineering education. In this paper, we discuss the three-pronged approach used in eLSI to: train a team of four teachers from each college and enable setting up of a robotics lab at each college. Analysis of feedback received from 64 teachers who participated in the pilot: (i) after the two-day workshop, (ii) at the end of e-Yantra Robotics Teacher Competition, (iii) during a visit to labs post lab inaugurations, and (iv) during a symposium held a year later for sharing and showcasing projects implemented in their robotics labs — shows that eLSI is effective in sustainable knowledge creation. The model used to establish robotics labs at 35 colleges across five regions of India in the current phase proves the scalability of the model.

e-Yantra Robotics Competition (eYRC) is an initiative of the e-Yantra project to bring the experience of Project Based Learning to engineering students by using a competition to deliver hands-on training on-line. Five hundred students forming 131 teams were selected from across India to participate in the pilot run of the competition, eYRC-2012 that consists of a set of tasks through which students are evaluated. We map the tasks such as theme analysis, implementation analysis, and video demonstration to outcomes such as acquiring basic knowledge, application of knowledge, and critical analysis. Results show that over 95% of the teams participated imbibed basic knowledge of embedded systems and robotics, 60% of the teams applied their knowledge to develop a solution to a given problem, while over 30% of the teams could critically analyze the problem and come up with an effective solution. These results are confirmed by similar effectiveness studies of the eYRC-2013 competition. This competition was conducted completely on-line and students did not incur any costs, ingredients essential for ensuring scalability and inclusiveness of the project. Students transfer the Robotic kits to their respective colleges at the end of the competition enabling nurturing of future generations of students at the colleges.

We present a high-level synthesis flow for mapping an algorithm description (in C) to a provably equivalent register transfer level (RTL) description of hardware. This flow uses an intermediate representation which is an orthogonal factorization of the program behavior into control, data and memory aspects, and is suitable for the description of large systems. We show that optimizations such as arbiter-less resource sharing can be efficiently computed on this representation. We apply the flow to a wide range of examples ranging from stream ciphers to database and linear algebra applications. The resulting RTL is then put through a standard ASIC tool chain (synthesis followed by automatic place-and-route), and the performance and power dissipation of the resulting layout is computed. We observe that the energy consumption (per completed task) of each resulting circuit is considerably lower than that of an equivalent executable running on a low-power processor, indicating that this C-to-RTL flow offers an energy efficient alternative to the use of embedded processors in mapping algorithms to digital VLSI systems.

Abstract A functional approach presents a fresh perspective on the problem of animation. We present an implementation of a functional animation system written in Haskell, and illustrate how it may be used to create simple and colourful animations.

Abstract We investigate the benefits of using a functional language to reason about and to implement animation. Since animation concerns pictures changing over time, we consider the manipulation of movies or picture sequences. A compact set of primitive operations over movies is introduced and its use as the basis for an animation system is illustrated. The notion of the behaviour of an animated character is formalised and we show how simple behaviours may be combined to create more complex behaviours. We then show how higher‐order functions allow us to construct a variety of useful tools which lead to a highly concise functional script.