This innovative practice WIP paper describes the implementation of a five-year mentoring initiative for science and engineering programs at an urban, private institution with the goals of providing holistic programming and opportunities for early career preparation for all first-year students. In partnership with Mentor Collective, peer mentor programming was designed to include data collection from mentees to better understand their needs and to support their academic training with meaningful experiences that support career readiness. Data over a five-year period from mentees demonstrate the following: (1) expectations from program participants vary significantly from learning how to maintain healthy work-life balance to succeeding in classes and participation in co-curricular activities, (2) graduation rates at and above 80% are achievable and sustainable, which is significantly higher than the national average and (3) closing of the opportunity gap can be accomplished for populations that have traditionally low graduation rates. Feedback from the mentors (industry professionals) reveal very high interest in their willingness to continue participation, which suggests that mentoring initiatives are mutually beneficial, sustainable, and scalable across institutions. Additional data from this report also demonstrate that the impact of linking first-year students with industry professionals can serve as a scalable mentoring solution.
This research-to-practice WIP paper describes an approach to engage high school students in research through the utilization of citizen science tools embedded with Machine Learning (ML) models. In the context of fostering early engagement in scientific research among high school students, this paper explores the integration of citizen science and ML using SmartCS, an existing platform for creating citizen science smartphone applications. The process requires no prior programming knowledge, making it accessible to a broad range of students. For our approach, a group of high school students participated in a two-month-long summer research program, where they were introduced to the principles of citizen science as a method for data collection across diverse scientific projects from different research domains. The program's initial task involved students in the conceptualization of a citizen science project, adopted based on a thorough literature review, followed by the practical task of developing a smartphone application for data collection and educational purposes. Students either created new datasets or curated existing ones to train lightweight ML models for computer vision tasks, specifically focused on providing visual guidance within these mobile apps. The final task involved deploying these applications for public use and collecting user feedback. Our experience suggests that this approach not only enabled students to learn aspects of computer science and engineering, particularly in the area of ML model training and mobile application software development, but also allowed them to experience firsthand the significant role citizen science can play in collecting and analyzing scientific data.
Rapid technological evolution and the advent of the Internet of Things (IoT) are reshaping the necessary competencies for professionals in different fields, including engineering that focuses on networking, telecommunications, IoT, or embedded systems. This research project proposes an in-depth analysis of the benefits brought about by the inclusion of LoRaWAN technology in the curriculum of courses offered by the Instituto Federal do Amazonas (IFAM), focusing on both technical skills (hard skills) and interpersonal and cognitive competencies (soft skills) of students participating in research, development and innovation project (R,D&I). LoRaWAN, a low power long-range communication technology, has significant potential for applications in smart cities, precision agriculture, natural resource management, and smart power meters, underscoring its relevance to current and technical education. LoRaWAN technology integrates various engineering disciplines in the curriculum, challenging the update process. The paper suggests a method to assess the performance of 12 students in three R, D, I projects for the improvement of the curriculum. Identifies the impact on students' education and provides guidance for educational institutions based on students' task difficulties and interaction with the development team.
Contribution: In this full research paper, we offer a quantitative analysis of how gamification influences students' gameful experience within a mobile online learning gamified platform, specifically Duolingo. We explored how these effects vary based on students' self-declared gender, providing valuable insights into the intricate dynamics within gamified educational environments. Background: The use of gameful environments as educational tools has been acknowledged for its potential to yield diverse outcomes in students' learning achievements, encompassing both advantageous and disadvantageous effects. Among the hypotheses regarding the observed diverse outcomes is that gamification may influence individuals' experiences differently based on their inherent traits, such as gamer type, age, and gender. While recent research has made strides in exploring this hypothesis, knowledge regarding how gamification can affect users' experience in gameful environments remains limited. Research Questions: This study aims to address the following question: How does gamification affect students' gameful experience (i.e., accomplishment, challenge, competition, guided, immersion, playfulness, and social experience) according to their self-declared gender? Methodology: To answer this research question, we conducted a quasi-experimental study (i.e., involving the comparison of groups without random assignment), organized in three steps. In the first step, we invited technology students to utilize the gamified platform, Duolingo, for a minimum of 20 minutes. In the second step, participants completed the GAME-FULQUEST, a scale comprising 56 items across seven dimensions, designed to measure their gameful experience while using the platform. They also answered a demographic questionnaire to indicate their gender (following ethical recommendations, we included the options: “male”,”female”, “non-binary” and “I prefer to not disclose”). In the third step, we employed Structural Equation Modeling to analyze the effects of gamification on students' gameful experience, according to their gender. Our sample comprised 110 students, with 42 self-identified as females and 68 as males. Findings: Our results indicate that learners' gender did not affect any of the dimensions of the gameful experience. This result opens up space for several insights into how gamification can affect the learners' experience according to different variables.
This research-to-practice full paper compares data science education strategies in China and the United States, exploring whether different approaches can achieve similar educational outcomes. In the U.S., data science programs are typically developed by individual schools to meet labor market demands, integrating data science as an interdisciplinary subject. In contrast, Chinese universities follow a uniform plan set by the education department, resulting in specialized fields like ‘Big Data Management and Application’ and ‘Data Science and Big Data Technology.’ This research collected data on course offerings and university rankings in China and the U.S., analyzing curriculum content and program characteristics. Major findings indicate that in the U.S., Data Science and Business Analytics programs focus on technical skills and are primarily found in top-ranking universities, while in China, Data Science and Big Data Management programs emphasize management and are more widely distributed across different ranking groups. Despite these differences, both countries show similar geographic concentration patterns in data science programs. The study concludes that China and the U.S. adopt different educational strategies but achieve comparable effectiveness in data science education.
