Search results

The conceptual framework of mathematical modeling (e.g., Lesh & Doerr, 2003) is a vital area in mathematics education research, and its implementation has potential for deeply involving children in integrated and meaningful learning. In mathematical modeling learners are active agents in content-integrated, real-world problem solving. This emphasis on integrating multiple content areas to answer big questions, the pursuit of mathematical modeling, descends from Dewey’s work. We present the definition, principles, and design of modeling practices for readers who may be familiar with early childhood curriculum but less so with using modeling for learning. We explore the application of mathematical modeling to early childhood classrooms and its compatibility with early childhood pedagogies and philosophies. Young children may often be underestimated, assumed to be unable to pose big questions that can be answered through activity, experience, and data; but we discuss how young children can be engaged in problems through mathematical modeling. Finally, as preservice teacher educators, we discuss preparing preservice and in-service teachers for modeling in their classrooms. We offer examples and guidance for early childhood teachers to engage in authentic practice – meeting children where their interests are and creating integrated problem-solving experiences.

Interest is currently growing in open social learner modeling (OSLM), which means making peer models and a learner's own model visible to encourage users in e-learning. The purpose of this study is to examine students' views about the OSLM in an e-learning system.

This case study was conducted with 40 undergraduate students enrolled in advanced programming and database management system courses. A Likert-type questionnaire and open-ended questions were used to obtain the students' views. System usage data were also analyzed to ensure the richness and diversity of the overall data set.

The quantitative data of the students' views were analyzed with descriptive statistics; the results are presented as graphics. The qualitative data of the students' views were examined by content analysis to derive themes. These themes are organized into four subtopics: the students' positive views, their negative views, their improvement suggestions and their preferences about using similar OSLM visualizations in other e-learning systems. The students' subjective views are discussed in the context of their recorded interactions with the system.

Competition due to seeing peer models was considered by participants both as positive and negative features of the learning system. So, this study revealed that, the ways to combine peer learner models to e-learning systems that promote positive competition without resulting social pressure, still need to be explored.

By combining open learner models with open peer models, OSLM enhances the learning process in three different ways: it supports self-regulation, encourages competition and empowers self-evaluation. To take advantage of these positive contributions, practitioners should consider enhancing e-learning systems with both own learner and peer model features.

Despite increasing interest in OSLM studies, several limitations and problems must be addressed such as sparsity of data and lack of study of different contexts and cultures. To date, no published study in this area exists in Turkey. The purpose of this study is to fill this gap by examining OSLM features in an e-learning system from the perspectives of Turkish students by using both their system interaction data and their subjective views.

Education is increasingly using Intelligent Tutoring Systems (ITS), both for modelling instructional and teaching strategies and for enhancing educational programs. The first part of the paper introduces the basic structure of an ITS as well as common problems being experienced within the ITS community. The second part describes WITNeSS ‐ an original hybrid intelligent system using Fuzzy‐Neural‐GA techniques for optimising the presentation of learning material to a student. The original work in this paper is related to the concept of a “virtual student”. This student model, modelled using fuzzy technologies, will be useful for any ITS, providing it with an optimal learning strategy for fitting the ITS itself to the unique needs of each individual student. In the third part, experiments focus on problems developing a “virtual student” model, which simulates, in a rudimentary way, human learning behaviour. Part four finishes with concluding remarks.

The retention and success of engineering undergraduates are increasing concern for higher-education institutions. The study of success determinants are initial steps in any remedial initiative targeted to enhance student success and prevent any immature withdrawals. This study provides a comprehensive approach toward the prediction of student academic performance through the lens of the knowledge, attitudes and behavioral skills (KAB) model. The purpose of this paper is to aim to improve the modeling accuracy of students’ performance by introducing two methodologies based on variable selection and dimensionality reduction.

The performance of the proposed methodologies was evaluated using a real data set of ten critical-to-success factors on both attitude and skill-related behaviors of 320 first-year students. The study used two models. In the first model, exploratory factor analysis is used. The second model uses regression model selection. Ridge regression is used as a second step in each model. The efficiency of each model is discussed in the Results section of this paper.

The two methods were powerful in providing small mean-squared errors and hence, in improving the prediction of student performance. The results show that the quality of both methods is sensitive to the size of the reduced model and to the magnitude of the penalization parameter.

First, the survey could have been conducted in two parts; students needed more time than expected to complete it. Second, if the study is to be carried out for second-year students, grades of general engineering courses can be included in the model for better estimation of students’ grade point averages. Third, the study only applies to first-year and second-year students because factors covered are those that are essential for students’ survival through the first few years of study.

The study proposes that vulnerable students could be identified as early as possible in the academic year. These students could be encouraged to engage more in their learning process. Carrying out such measurement at the beginning of the college year can provide professional and college administration with valuable insight on students perception of their own skills and attitudes toward engineering.

This study employs the KAB model as a comprehensive approach to the study of success predictors. The implementation of two new methodologies to improve the prediction accuracy of student success.

The purpose of this paper is to develop a quantitative model of problem solving performance of students in the computer-based mathematics learning environment.

Regularized logistic regression was used to create a quantitative model of problem solving performance of students that predicts whether students can solve a mathematics problem correctly based on how well they solved other problems in the past. The usefulness of the model was evaluated by comparing the predicted probability of correct problem solving to the actual problem solving performance on the data set that was not used in the model building process.

The regularized logistic regression model showed a better predictive power than the standard Bayesian Knowledge Tracing model, the most frequently used quantitative model of student learning in the Educational Data Mining research.

Providing instructional scaffolding is critical in order to facilitate student learning. However, most computer-based learning environments use heuristics or rely on the discretion of students when they determine whether instructional scaffolding needs be provided. The predictive model of problem solving performance of students can be used as a quantitative guideline that can help make a better decision on when to provide instructional supports and guidance in the computer-based learning environment, which can potentially maximize the learning outcome of students.

One way to improve the quality of business and software processes is to make them compliant with standard. This paper seeks to explore the suitability of the BPMN and the teaching method for process modeling, which uses SEI CMMI‐SW standard as input and produces software process models as output.

The research approach consists of two parts. First, the students' responses during practical lectures of process modeling were gathered and studied. Then, the teaching process was modified and students' responses were studied again. The second part involves the survey about students' attitude towards BPMN usability and ease of use.

There are two key findings of the study. First, using the SEI CMMI‐SW standard in early phases of software process modeling is not very effective, because it overloads the students with abstract requirements, which are included in the standard. The more effective way is to use the standard in later phases of process modeling, in the phase of assessment and improvement of the process model. The second key finding is that there is a positive attitude among students towards BPMN usage for process modeling.

There is no evidence that this teaching approach can be generalized beyond the population of information science students and academic environment.

The improved teaching method for business and software process modeling can be used in similar environments (practical lectures of process modeling). Although there is no evidence, quality managers from organizations can also benefit from the lesson learned and avoid a less suitable process‐modeling approach.

The paper is useful to all roles in education and businesses, who deal with process models and standards (teachers, business and software analysts, quality managers, process designers).

Researchers have previously utilized the project-based 6E learning model and the problem-based quantum learning model in various courses, such as the instructional principles and methods course and the character and values education course. These models were evaluated for their impact on students in different subjects, including developing skills, values, democracy perceptions, attitudes towards cooperative learning, metacognitive thinking skills and teacher self-efficacy perceptions. In 2023, Ökmen, Sahin and Kiliç reported positive outcomes, while Sahin and Kiliç reported similar findings in 2023a, 2023b and 2023c. There has been no investigation into how the models affect students' critical thinking and academic literacy. This study seeks to determine the impact of both models on these skills, gain more insight into their effectiveness and determine which is more beneficial. The results will guide the decision-making process for the character and values education course and other courses in the future. Specifically, this research aims to compare the effects of the project-based 6E learning model and problem-based quantum learning model on critical thinking and academic literacy.

This research employed the Solomon four-group experimental design to assess the efficacy of the applications. Prior knowledge and experience of the participants were evaluated through pretests. However, it should be noted that pretests may impact posttest scores either positively or negatively. For instance, participants taking the test multiple times may become more interested or attentive to the subject matter. The Solomon four-group design was deemed appropriate to analyze the influence of pretesting. This design enables the investigation of the application effect, pretest effect and interactive effect of pretest and application (van Engelenburg, 1999).

It was concluded that the project-based 6E learning model was effective in developing critical thinking in students, but not significantly. It was concluded that the problem-based quantum learning model significantly improved students' critical thinking skills. It was concluded at the end of the study that the project-based 6E learning model notably enhanced students' academic literacy. It was concluded that the problem-based quantum learning model had a significant positive impact on students' academic literacy. According to research, it has been determined that the problem-based quantum learning model is superior in enhancing critical thinking abilities compared to the project-based 6E learning model. Nevertheless, there seems to be no detectable disparity in the academic literacy advancement of pupils between the problem-based quantum learning model and the project-based 6E learning model.

There has been no investigation into how the models affect students' critical thinking and academic literacy. This study seeks to determine the impact of both models on these skills, gain more insight into their effectiveness and determine which is more beneficial. The results will guide the decision-making process for the character and values education course and other courses in the future.

As the US transitions to a majority–minority population, the underrepresentation in the science, technology, engineering, and mathematics (STEM) workforce must be resolved to ensure that our nation maintains its competitiveness and global economic advantage. The persistent problem of retaining underrepresented minority (URM) students in STEM continues to be a national priority after several decades of attention. The role of historically black colleges and universities (HBCUs) in addressing this challenge cannot be overstated, given their history in producing African American STEM graduates. As the largest HBCU in the country, North Carolina A&T State University (NC A&T) serves a combined undergraduate and graduate population of 11,877 students, 78% of which self-identify as African American. To overcome the multiple challenges that impede retention and persistence to degree completion in biology, the Department of Biology at NC A&T has adopted a major cultural shift in its advising strategy. The new approach encompasses a Life Mapping and Advising Model that builds faculty–student relationships and engages both parties effectively in the process. The model includes six important pillars to drive student success: (1) dedicated advising space, the Life Mapping and Advising Center (LMAC), (2) effective advisors, (3) integrated peer mentor and peer tutoring programs, (4) an intrusive advising strategy, (5) integration with first-year student success courses, and (6) life coaching. Although the program is in its infancy, based on the first-year assessment data, we have observed many promising trends that, together, point toward successful retention and persistence of our students in the major.

Entrepreneurship education has been largely treated as a pedagogical “black box.” Despite the emergence of popular entrepreneurship models such as business planning, the lean startup, or business model canvas, neither theoretical nor pedagogical foundations are typically evident. This limits the accumulation of useful evidence that could inform better teaching practices. In this chapter, we develop a set of conceptual models anchored in learning theory regarding how entrepreneurship education should be taught to students. These conceptual models are built on the techniques of entrepreneurship pedagogy such as experiential education. They are developed for three groups of students: students without any entrepreneurship experience, students with previous entrepreneurship experience, and students who are currently running their start-ups. A set of potential variables that could be used for course evaluation purposes is also included. The proposed models meet the needs of students with different levels of entrepreneurship experience. Theoretically, we demonstrate that entrepreneurship students should not be treated as a homogeneous group, as they have different levels of startup experience and different educational needs. Lecturers of entrepreneurship programs could choose the suitable model proposed in this chapter in teaching based on the characteristics of their students. The chapter provides novel insights with regard to how entrepreneurship programs should be designed for students with different levels of entrepreneurship experience.

Many instructors have implemented decision-based learning (DBL) into their courses. This chapter is a careful qualitative analysis of the narratives in this book done by the editors. The author found common themes among all the narratives. The first theme was that many instructors discovered that they were missing conditional knowledge in their instruction. Second, the author found common issues around the complexity of designing an expert decision model (EDM). Included in this theme are stories about selecting problems and organizing the EDM, building the EDM around specific course learning outcomes, providing just-enough, just-in-time instruction, and introducing the decision model and software to students. Instructors also discovered that assessing the learning of students needed to go beyond traditional goals and began to include new goals related to conditional knowledge. Finally, the author describes the comments made by both faculty and students about the experience of using DBL. Several authors described the value of using DBL in the process of taking students from novice thinkers to expert thinkers. Many students expressed that they enjoyed the process that DBL presented to them and that they had a new level of confidence to be able to approach problems in the content area. Summaries and quotes from the chapters in this book are referenced by the authors’ names and the content areas they were teaching.