Seeking consensus: Defining foundational concepts for a graduate level introductory program evaluation course

Despite significant research supporting active learning, many professors continue to use traditional lectures as their primary teaching method, particularly in introductory level courses. This article explores whether jigsaw cooperative learning had a positive impact on student grades and enhanced their learning experience, as compared to the traditional lecture method. The question was answered by collecting data from an insurance and risk management introductory course in the business school. To answer the question on learning experience, students completed a validated survey on each pedagogy, consisting of 15 statements that they rated on a Likert scale of 1 to 5, strongly disagreeing or agreeing with the statements. The course content was taught using lectures for four learning modules and the jigsaw learning method for four learning modules. After each module, a quiz was written by each student, and these grades were compared to establish the impact of each teaching method on student grades. Data was analyzed using descriptive statistics and two-way ANOVA testing to determine significant differences. Data was collected from two student groups. One group was a traditional university group of diverse students and the other group consisted of international students from India. I compared the results of the two student groups to identify any differences. This research adds to the studies on active learning in insurance education, specifically jigsaw cooperative learning. It also contributes to literature on effective teaching strategies for international student groups.

This paper describes an action research for developing better algorithmic thinking in introductory programming courses. Enhancing algorithmic thinking and improving problem solving skills are the main objectives of such courses and are considered a significant success factor in future courses as well as future work achievements. Furthermore, failing to develop these skills, usually leads to failures in these introductory courses as well as the courses that follow. As part of this action research, that was performed during four semesters several course structures and learning tactics were examined. The evaluation methodology was simple and based only on the percentage of failing students. The success achieved was attributed to two main factors (1) using a visualization environment (Micro-world) for the whole duration of the course, which helped in understanding the more complex and abstract issues, and (2) using individual assignments that enforced better learning habits and development of individual algorithmic thinking. The paper describes the various attempts, as well as the final structure, that reduced the failing students by over 77%. This paper describes an action research for developing better algorithmic thinking in introductory programming courses. Enhancing algorithmic thinking and improving problem solving skills are the main objectives of such courses and are considered a significant success factor in future courses as well as future work achievements. Furthermore, failing to develop these skills, usually leads to failures in these introductory courses as well as the courses that follow. As part of this action research, that was performed during four semesters several course structures and learning tactics were examined. The evaluation methodology was simple and based only on the percentage of failing students. The success achieved was attributed to two main factors (1) using a visualization environment (Micro-world) for the whole duration of the course, which helped in understanding the more complex and abstract issues, and (2) using individual assignments that enforced better learning habits and development of individual algorithmic thinking. The paper describes the various attempts, as well as the final structure, that reduced the failing students by over 77%.

The scientific enterprise holds the most promise for addressing urgent societal challenges when diversity, inclusivity, and collaboration are fostered within the science community. This requires that scientists develop personal and interpersonal competencies that allow them to participate in diverse, inclusive, and collaborative endeavors. Within STEM education, persistence and success in science and math are also influenced by such personal and interpersonal experiences. The personal connection students feel to the class material, the nature and tone of interactions among students and faculty, the sense of agency that students develop around accessing the “hidden curriculum” (office hours, research experiences, and other academic support or leadership experiences), and the sense of mattering and belonging, are all important factors in helping students develop an identity as a scientist. At our small liberal arts college, a survey of 800 students in science and math courses revealed that women, students of color, first generation to college students, and students from low socioeconomic status backgrounds disproportionately report these factors as barriers to their academic success in science and math classes. We are exploring whether we can foster a diverse, inclusive, and collaborative community of science while also lowering barriers to student success through the creation of opportunities for students and faculty to come together in interdisciplinary Grand Challenge learning collectives. Learning collectives tacklecomplex societal issues that students care deeply about and which can be addressed only through collaboration among different disciplines and lived experiences. We report on outcomes and lessons learned from a Grand Challenges learning collective focused on Global Climate Change. A set of linked introductory STEM courses (Calculus, Physics, Chemistry, Data Science, Psychology) and freshman writing seminars (Sociology, History, Earth Science) addressed climate change from their own disciplinary perspectives while providing opportunities for students to engage in climate change work in foundational science courses. Work inside classes was supported by community conversations and events outside of class. At the conclusion of the introductory level courses, students explored opportunities to continue building the community by serving as near-peer mentors, by engaging in student research experiences or community-engaged learning experiences, or serving as a student Catalyst working to improve inclusion. Fundamental to the evolution of the learning collective was a program of professional and personal development for faculty and students. Working groups, formed around topics of common interest, aimed to build capacity for inclusion and equity. Faculty participants expressed the need for sustained support as they examined their teaching practices and experimented with ways to foster greater inclusivity. Students deeply valued out-of-class conversations and interactions with faculty that humanize the scientific enterprise and that help them foster their own identity as a scientist.

The college students who have relatively weak academic background feel more difficult in learning programming language grammars and programming skills in introductory course. At the end of semester, most of students had the negative attitude to programming and only a few students could write the programs for the given problems because they spent most of time to learn grammars instead of learning problem solving skills and logics. In this study, we propose to use Scratch in introductory programming course to help students to understand grammars and problem solving skills. It's necessary to educate first-time programmers how to solve the problems before they learn grammars of the programming language in their first programming language course. This paper shows that Scratch allows students not only to learn problem solving skills in programming but also to motivate students themselves in the class.

In order to rectify the historical marginalization of Black workers in computing fields, many academic and industrial initiatives have been established to increase the diversity and inclusion in the tech industry. In this study, we explored how one particular post-secondary education program at one historically black institution known as the Google in Residence (GIR) program impacted students’ interests and their pursuit of computing-related careers. Data on employment outcomes at this institution showed that the GIR participants who obtained internships and full-time employment at Google were predominantly international students, not African American students who make up the majority of the undergraduate student and about half of the GIR participants. To understand this phenomenon better, we interviewed 27 students who spanned all 4 years of study and included African American and international students. The interviews were structured to elicit a wide range of factors influencing career decisions. Our analysis describes the impact of a particular GIR introductory-level programming course and other GIR program impacts beyond the introductory-level course. We found that the GIR introductory course was engaging, inspired students to change their degree in a more computing-oriented direction and supported internship applications. The informational workshops, networking events, mock-interviews, and advising of the computer science club, which were supported by the GIR program, built students’ confidence in interviewing and obtaining full-time employment within the tech industry. While the GIR course and program had a positive influence on students of all backgrounds, the reputation of Google was especially motivating for international students. However, the African American students in this study generally had career aspirations and goals that were less tied to specific companies or industries. Based on our findings, potential improvements could include broadening awareness and access of the GIR course to non-computer science majors, offering summer programs to increase students’ computational confidence, and increasing the diversity of GIR and computer science instructors.

This paper describes an introductory course on biologically based sub-symbolic machine learning paradigms. Specifically, this paper covers Artificial Neural Networks, Genetic Algorithms and Genetics-Based Machine Learning. It provides the structure, motivation, content, texts and tools for the course. This course is suitable for an upper division undergraduate level course or as an introductory graduate course. The paper includes a section on bibliographical references to aid the instructor in preparing for this course.

In the authors' experience the languages available for teaching introductory computer programming courses are lacking.In practice, they violate some of the fundamentals taught in an introductory course.This is often the case, for example, with I/O.Picky is a new open source programming language created specifically for education that enables the students to program according to the principles laid down in class.It solves a number of issues the authors had to face while teaching introductory courses for several years in other languages.The language is small, simple and very strict regarding what is a legal program.It has a terse syntax and it is strongly typed and very restrictive.Both the compiler and the runtime include extra checks to provide safety features.The compiler generates byte-code for compatibility and the programming tools are freely available for Linux, MacOSX, Plan 9 from Bell Labs and Windows.This paper describes the language and discusses the motivation to implement it and its main educational features.

The ideal new hire should be strong in fundamental principles and contemporary engineering practices. With the complexity of CAD tools increasing rapidly care should be taken to not over-emphasize practices. Basic principles must not be sacrificed for the teaching of engineering practices, especially in introductory level courses where the principles are prerequisite to many courses in the curriculum.

Introductory level courses in many Science, Technology, Engineering, and Math (STEM) disciplines require students to acquire an enormous new discipline-specific vocabulary in preparation for future courses. Students are often not prepared for the amount of self-directed studying they must do to be successful in these types of courses, particularly in their first year of college. To assist students in more accurately gauging their readiness for an exam, online low-stakes quizzes were implemented in a freshman level course, giving students an opportunity to practice their new language with minimal grade-related consequences. This quizzing strategy provides students an opportunity to self-assess their current level of knowledge. The quizzes also provide feedback, helping students determine how to adjust their behavior to ensure that acquisition of the missing knowledge is successful. This paper presents preliminary results of the research effort and illustrates the effects of this low-stakes quizzing. Specifically, this paper evaluates (1) whether the quizzing helps students to better prepare for medium and high stakes exams; (2) whether the quizzing increases the mean exam scores compared to previous semesters with no quizzing; and (3) whether the quizzing must be required (low-stakes) to be effective, or whether it can be optional (no-stakes) and still benefit students.

In 1991 over 38% of all U.S. college students, and nearly 42% of female U.S. college students, were age 25 or older. According to projections, by 1998 the number of nontraditional (age 25 or older) U.S. college students will rise to almost 46% (U.S. Bureau of the Census, 1995, p. 178-179). The Education Resources Institute (1996) reported that the percentage of college students age 40 or over grew from 5.5% of the total enrollment in 1970 to 11.2% in 1993, making this group the fastest-growing age category during that period. Clearly, nontraditional students will continue to be an increasingly significant part of the college classroom. Despite their significance, nontraditional college students have been largely ignored in investigations of classroom discussion. Much of the debate has centered around the issue of student gender in the college classroom with little consideration to the impact of student age on classroom participation. The Education Resources Institute (1996) report described the typical 40 and over student as a white female who attends a community college part-time and works thirty or more hours per week. Thus, as Howard, Short, and Clark (1996) suggested, due to their different life experiences, nontraditional students' definition of the situation in the college classroom is likely to differ considerably from that of traditional students. These differences contribute to the greater willingness of nontraditional students to participate in classroom discussion. Because Howard, Short, and Clark's (1996) sample of 13 introductory level courses were taught almost exclusively (12 of 13 courses) by female instructors, it was not possible to consider the effect of instructor gender or course level in the mixed-age classroom. This study seeks to fill that void in the literature and contribute to the ongoing debate regarding the impact of student age, student gender, instructor gender, and course level on student participation. Previous Research Since Hall and Sandler's (1982) contention that the college classroom presents a chilly climate for female student participation, the issue of student gender has been the object of considerable study with ambiguous results. Several studies found no effect of student gender (Boersma, Gay, Jones, Morrison, & Remick, 1981; Cornelius, Gray, & Constantinople, 1990; Crawford & MacLeod, 1990 [in their university sample]; Hamlin & Janssen, 1981). However, other studies have concluded that males participate disproportionately (Brooks, 1982; Constantinople, Cornelius, & Gray, 1988; Crawford & MacLeod, 1990 [in their small college sample]; Fassinger, 1995; French, 1984; Graddol & Swann, 1989; Karp & Yoels, 1976; Pearson & West, 1991; Spender, 1981). Brooks (1982) linked males' greater levels of participation with instructor gender, suggesting that males participate more than females in female-taught classes. On the other hand, Sternglanz and Lyberger-Ficek (1977) and Pearson and West (1991) concluded that male students are more likely to dominate discussion in male-taught classes. Auster and MacRone (1994) found that instructor gender was not a significant determinant of student participation. Fassinger (1995) concluded that although instructor gender had no effect on the participation of male students, female students were more likely to participate in classes taught by female instructors. Class size was found to be a significant determinant of participation in classroom discussion in several studies (Constantinople, Cornelius, & Gray, 1988; Crawford & MacLeod, 1990; Fassinger, 1995; Howard, Short, & Clark, 1996). Fassinger (1995) emphasized the role of student traits (e.g., confidence, comprehension, interest, and preparation) and class traits (size, emotional climate, interaction norms, frequent large group discussions) over instructor traits (e.g., gender). Nunn (1996) challenged Fassinger's (1995) findings by concluding that the instructor's choice of teaching techniques (e. …

The introductory level course Geography of the U.S. and Canada requires students to grasp large amounts of complex material, oftentimes using a lecture-based pedagogical approach. This article outlines two ways that popular music can be successfully used in the geography classroom. First, songs are used to review key concepts and characteristics of each subregion as a way to reinforce course material. Second, students critically analyze place representations in a selected song as a way to synthesize course themes. Excerpts from student work demonstrate that music does assist in helping students connect to and understand geographical concepts.

In 2019, a national group of local food system educators and practitioners identified over 140 foundational core competencies critical to local food system development work and began to identify existing educational resources related to these competencies. This process resulted in a new aggregated resource: the Local Food System Practitioner and Educational Resource Database. Included in this database is a core competency matrix that distinguishes three levels of learning for each competency so that practitioners can identify learning opportunities most closely tailored to their educational needs. It also serves as a framework and competency matrix for educators to use to help assess and communicate the learn­ing out­comes of their curricula. This framework is the overall concept for understanding the compe­ten­cies, and the matrix is the tool developed to assess and evaluate the level at which an educa­tional resource teaches a competency. In this article we apply the newly create core competency matrix to two existing local food system develop­ment courses. We share lessons learned from applying the matrix and insights gained from com­paring two introductory level courses. We con­clude with recommendations for improving the resource database and matrix to a more user-friendly model for educators and local food system practitioners.

The Bologna process is intended to culminate in the formation of the European Higher Education Area (EHEA) by 2010. Its aim is to facilitate the mobility of people, the transparency and recognition of qualifications, quality and development of a European dimension to higher education, and the attractiveness of European institutions for third country students.This paper provides an overview of progress towards implementation in EHEA member states using official documents and interview data from faculty teaching computing in countries represented at the ITiCSE 2006 meeting. The key areas where the structures established by the Bologna process are problematic for computing education arise from the rapidly changing nature of the curriculum. It seems that the maturity and capability criteria, as well as the manner in which learning outcomes are specified, being developed within the Bologna process are too general. This endangers the properties of transparency and mobility that the process intends to promote.Progression and prerequisite knowledge in computing degrees can be very specific. For instance, generic learning outcomes for an introductory programming course quite rightly will not specify the programming language, or languages, used to implement algorithms. However, suppose a student intends to study an advanced algorithms and data structures course in which Java is the language of implementation which has an introductory course in programming as a prerequisite. If the introductory course language was Standard ML it is not clear that the prerequisite course actually provides the student with a suitable background. These types of complexities are typical of computing, where early subject curricula are not standardised nationally or internationally, and create significant hurdles for realising the Bologna objectives.

Geographic Information System (GIS) skills are increasingly marketable across a wide range of industries, subject areas and specialized fields. As a result, GIS courses draw students from a plethora of disciplines beyond geography, including business, social sciences, agriculture, geology, natural resources and computer science. At advanced teaching levels, this disciplinary diversity generates questions about how complex GIS skills are being taught to students who do not necessarily have a background in geography or the spatial sciences. This study compares undergraduate and graduate course curricula to determine what topics, techniques and theory are being addressed and prioritized in GIS courses across pedagogical levels. We surveyed 1,698 courses being taught at 126 research universities in the United States. Our findings point towards a shift in pedagogy that favours technique- or application-centred lessons at advanced levels that require less background knowledge in geography than more theory-centred teaching methods. Our findings also suggest that a large number of graduate courses, even introductory courses ones, fail to address many fundamental GIS concepts and theories. Together, the results suggest a need for standards to ensure that students enrolled in GIS classes are properly trained to enter the workforce with appropriate skills.

Introductory programming classes continue to be challenging for students. Early attempts in teaching introductory courses focused on teaching language syntax and expanding to procedures for organizing large programs. Later, the object-first approach gained popularity focusing on the object-oriented nature of modern programming languages. We advocate a toolchain-first approach to teaching programming where the tools used in writing, debugging, compiling, formatting, versioning and testing the code are given as much importance as the language being taught. We have successfully integrated industry standard tools into our introductory programming courses. The tools provide the scaffolding to students as they develop their code. By prioritizing tools and tool based help as an integral part of programming, we prepare students for more complex tools, such as LLM based AI companions, that will be used to produce software.