environments. 3. Idea: Relationship Between Student Engagement and Learning Outcomes • Importance: Investigating the relationship between student engagement and learning outcomes can help in designing effective educational programs. 4. Idea: Integration of Real-world Applications in Curriculum Design • Importance: Enhancing the relevance and efficacy of educational programs through the integration of theoretical knowledge with real-world applications. 5. Idea: Long-term Impact of COVID-19 on Technology Education • Importance: Understanding the long-term impact of the COVID-19 pandemic on education is essential for future educational planning, especially in technology
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needed help to struggling students is,particularly, important. For higher education institutions, early detection of at-risk students isessential for planning and providing the appropriate remedial services that students need in atimely manner.Various approaches to student performance prediction have been explored. Some studies requiredesigning specific randomized experiments [1], [2], [4], [6], while others, like this study, focuson utilizing data gathered by ubiquitous Learning Management Systems (LMSs) based onstudent activities and interactions with course materials [3], [8], [9]. Additionally, some studiesaim to evaluate the efficacy of certain teaching methodologies [4], [5], while others seek toidentify problems early in the semester to
most students in our sample verbalized SCH and SCL strategies,they were unable to go a step forward, such as generate a hypothesis or test a hypothesis, whichcharacterizes debugging for expert programmers [17]. In addition, all students in this study wereable to develop a plan involving more than one debugging strategy, which characterizes expertprogrammers [16].Finally, the importance of debugging for software development and the preparedness of ourstudents for the workplace is of foremost importance [4]–[6], [33]. While we explore ways tohelp students acquire expert programming levels as early as possible, acknowledging theimportance of debugging in teaching others, more complex programming skills are critical [15].Individual practice may
. LaFerriere, “Enabling Meaningful Labor: Narratives of Participation in a Grading Contract,” J. Writ. Assess., vol. 13, no. 2, p. 1, 2020, doi: 10.35360/njes.316.[12] A. M. Shubert, “Contracts for a Time of Crisis : What I Learned from Grading in a Pandemic,” vol. 1, no. 17, 2021.[13] T. S. Harding, M. J. Mayhew, C. J. Finelli, and D. D. Carpenter, “The Theory of Planned Behavior as a Model of Academic Dishonesty in Engineering and Humanities Undergraduates,” Ethics Behav., vol. 17, no. 3, pp. 255–279, Sep. 2007, doi: 10.1080/10508420701519239.[14] T. VanDeGrift, H. Dillon, and L. Camp, “Changing the Engineering Student Culture with Respect to Academic Integrity and Ethics,” Sci. Eng. Ethics, pp. 1–24, Nov. 2016, doi:10.1007
approaches and studentcognitive development. An additional challenge associated with ChatGPT usage, as argued in [22],lies in the potential for learners to rely on AI models for task completion excessively. This over-reliance can potentially impact their critical thinking and problem-solving skills, fostering a senseof lethargy and indifference towards independent investigation. ChatGPT stands as a versatile tool with the potential not only to aid students in their learningjourneys but also to offer valuable assistance to tutors and educators across a spectrum ofapplications. In [23], diverse ways educators can harness this tool in teaching have been delineated.Notably, it proves beneficial in personalized learning, lesson planning, language
, particularly in how we introduce and integrate the notional machineconcepts across the curriculum.The integration of Notional Machine-guided labs in CSE12, including new assignments on Linked Listsand Hash Maps, reflects our ongoing commitment to this pedagogical approach. As we plan to expandthese labs to include more complex data structures, it is crucial to balance the depth and breadth of theseconcepts without overwhelming the students. While the direction in CSE12 has been promising andaligns with our initial observations, continuous refinement of the lab assignments and statistical tests,robust data collection, and feedback-driven iterations are essential. Our journey in enhancing theefficacy of the Notional Machines approach is ongoing, and we
is that lowerincome students have significantly different perceptions regarding the risks and opportunities oftheir career pathways [14], [15]. Kapoor & Gardener-McCune [11] found that computingstudents with lower socioeconomic backgrounds found it difficult to pursue industry internshipdue to family and other circumstances. Krenz et al. [16] indicated that lower-income computingstudents had difficulty pursuing graduate school full-time due to familial and economicresponsibilities. To better support lower-income students in computing to broaden theirparticipation in computing careers, it is critical to understand these students’ viewpoints on jobfactors associated with different career pathways for their post-graduation plans.3. Research
. Similarly, ACCESS students appear to haveincreased their confidence in being able to approach a faculty or staff member to get assistancewith academic problems between the 2021 and 2022 surveys. This result may be explained bythe changing population of ACCESS students. All 2021 survey respondents were in their firstyear of the ACCESS program, while among 2022 survey respondents some students were intheir first and other in their second year of the ACCESS program. The variation of time in theACCESS program, along with the natural maturity gained by completing another year of collegeand life, may explain some of the increase.Future work may include augmenting the survey with additional questions related to the sense ofbelonging. Furthermore, we plan
builtinto smart phones such as Siri, was a polarizing issue for most of the participants. Thirteenparticipants have VAs installed in their homes and use them regularly, expressing satisfactionwith how well they worked. Five of the remaining nine participants that did not use VAs werequite adamant that they did not use them and were not planning to do so, citing privacy concernsof installing passive monitoring devices in their homes. A wide variety of smart devices were mentioned by participants. The most commondevice, mentioned by twelve participants, was smart outlets that were used primarily to remotelycontrol Christmas lights, regular lights, and fans. Seven participants discussed security-relatedsmart devices such as Ring doorbells