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This study reviewed the evolving capabilities of hydro-drones, Unmanned Aerial Vehicles (UAVs) which are used for spraying and cleaning with a focus on water management, and it revealed that the unique capabilities of hydro-drones offered the potential to create challenging and rewarding education and training programs, designed to provide new or expanded employment opportunities and related career paths for adventuresome individuals in the growing field of drone technology. Overall, the study found that hydro-drones are having a dramatic transformational impact on many industries, especially their cleaning and sanitizing programs, while creating a demand for employees with the requisite education and training background in drone technology. The proverbial door is opening wide for higher education, trade organizations, and professional associations to develop and to offer innovative programs covering drone technology, management and entrepreneurship. Included in such courses could be added exposure to legal and regulatory compliance, drone safety, and drafting a drone flight plan, as required by the Federal Aviation Authority (FAA).

This presentation highlights the challenges of establishing a consistent application of the two terms, "threats" and "hazards," in academic courses and professional training, designed for emergency, disaster, and humanitarian management programs offered by public and private higher education, government agencies, and nonprofits. A new approach for viewing threats and hazards, named Energetic Disrupter Models, is presented, based on their ambient sources of energy, specifically potential or kinetic. Examples of threats and their collateral hazards for naturally occurring events are also offered for discussion and discernment. Such examples can provide the ingredients to created dynamic case studies that reimagine threats and hazards and simultaneously incorporate their respective sources of energy, which can create impactful events.

The use of Bayesian Knowledge Tracing (BKT) models in predicting student learning and mastery, especially in mathematics, is a well-established and proven approach in learning analytics. In this work, we report on our analysis examining the generalizability of BKT models across academic years attributed to "detector rot." We compare the generalizability of Knowledge Training (KT) models by comparing model performance in predicting student knowledge within the academic year and across academic years. Models were trained on data from two popular open-source curricula available through Open Educational Resources. We observed that the models generally were highly performant in predicting student learning within an academic year, whereas certain academic years were more generalizable than other academic years. We posit that the Knowledge Tracing models are relatively stable in terms of performance across academic years yet can still be susceptible to systemic changes and underlying learner behavior. As indicated by the evidence in this paper, we posit that learning platforms leveraging KT models need to be mindful of systemic changes or drastic changes in certain user demographics. [For the complete proceedings, see ED630829. Additional funding was provided by the U.S. Department of Education's Graduate Assistance in Areas of National Need (GAANN) program.]

This study aimed to build a model to detect the factors to enhance student engagement and learning development in mobile learning during the COVID-19 Pandemic. Data from a total of 400 junior-high-school students were collected in China in the fall semester of 2020, and a large proportion of students preferred accessing their study with cellphones (67.0%) than with computers (11.8%), laptops (11.3%) or pads (10.0%). Exploratory factor analysis and structural equation modeling (SEM) were used for data analysis. The fitness of the items for each scale of the student engagement and learning development indicated a sufficient fit (X[superscript 2][subscript (19)] = 41.252, GIF = 0.974, AGFI = 0.951, CFI = 0.986, RMSEA = 0.054, NNFI = 0.979, IFI = 0.986). The results of SEM analysis show that emotional engagement is the most important factor (r[superscript 2] = 0.859) in the model, and student engagement has a significant positive impact on learning development in mobile learning. The findings of this study provide a good reference for enhancing student engagement or fostering students' learning development in mobile learning. [For the full proceedings, see ED639391.]

Co-curricular service-learning has increasingly attracted participants from students as it can enrich their experience of learning chemistry, among others. An example is "Kimikamahika," wherein chemistry students from a Catholic university in the Philippines volunteer in public schools, orphanages, daycare centers, and local organizations to promote interest in chemistry among children by demonstrating eye-catching experiments. However, studies are few on the lasting impression of co-curricular service-learning among students compared to their peers without such community engagement. Hence, this mixed-methods study assessed the long-lasting impact of "Kimikamahika" on its past volunteers vis-a-vis their peers not part of this service-learning in terms of gains in course-related skills, real-world application, career opportunities, leadership skills, and community engagement. A one-tailed Mann-Whitney U Test ([alpha] = 0.05) of the survey responses revealed significantly higher ratings given by 54 volunteers of "Kimikamahika" in the acquisition of course-related skills (p = 0.033), application of classroom learning to real-world situations (p = 0.045), and participation in community engagement (p = 0.013) against the ratings of 24 peers not involved in this service-learning. A thematic analysis of interviews with thirteen past "Kimikamahika" volunteers further showed the lasting impression of a cocurricular service-learning on their academic, personal, professional, and civic life. [For the full proceedings, see ED654100.]

This paper discusses the steps involve in project development for developing the mobile application, namely Blood Bank Application and developing the convertor for software testing. The project development is important for Computer Science students for them to learn the important steps in developing the application and testing the reliability of the application. The first step involved is the development of the mobile application. Then the convertor is developed to convert the mobile application in ".apk" format into Java program in ".java" format. The java program is then tested under Eclipse environment using JUnit. Finally, the Java program is tested for its capability of generating test cases using SenaTLSParser. The comparison of the results of the time taken to produce test cases is presented using Junit and SenaTLSParser. Based on the results, SenaTLSParser is more reliable compared with JUnit since its response time is less than JUnit. The whole steps involved in this project development are discussed in this paper. [For the complete proceedings, see ED655360.]

Linggle is a pattern-based referencing tool that assists in collocation learning. In this ongoing project, we aimed to improve its performance further. First, many of the example sentences are long and difficult for students to understand, so we used a machine learning method and trained a classifier to help select dictionarylike example sentences. Second, we created a database of 60,270,000 sentences from 4C, S2ORC, and VOA Learning English. We also included Google books for real-time supplements. Then, we applied the classifier to select good example sentences from the database for display. We also limited the number of example sentences displayed for search results to improve users' experiences. Two classes of English as a Foreign Language (EFL) college students (N=51) were invited to use the enhanced tool and filled out a questionnaire. The results showed that the students were positive about Linggle's new interface and the quality of the example sentences. We expect that more EFL learners will benefit from the tool. [For the complete volume, "Intelligent CALL, Granular Systems and Learner Data: Short Papers from EUROCALL 2022 (30th, Reykjavik, Iceland, August 17-19, 2022)," see ED624779.]

The colleges and universities in Taiwan are confronted with the following two problems: (1) The average youth (aged 15-29) unemployment rate is more than double the overall unemployment rate; and (2) Mainly affected by the low birth rate, the admission and vacancy rates of colleges and universities have increased, and fierce competition exists among colleges and universities. This paper aims to answer the following two questions raised in Taiwan: (1) Does students' participation in the study opportunities created by university-industry cooperation really help the success rate of future employment? (2) In addition to cultivating cross-discipline talents, how can universities maintain their sustainable competitiveness? Based on a literature review and working experience, the author positively states that internship experience can improve students' employment rates, earnings, and job satisfaction after graduation. After that, several successful internship experiences and governmental promoting measures in Taiwan are mentioned. Four key success factors of internship are also described as follows: a clear structure, meaningful roles, paid internship, and a supportive environment. In terms of the second question, the author argues that the following measures should be taken: (1) Frequent inventory and analysis of the sources and quality distribution of students recruited by every academic unit; (2) Timely analysis of the resource allocation, financial revenue, and expenditure of academic units; (3) Appoint teachers based on expertise, and promote 360-degree feedback in the evaluation of teaching; (4) Strengthen the cultivation of STEM professionals; (5) Develop students' core foundations and transformative competencies; and (6) Appropriately manage world university rankings as needed.