Response to Intervention in the State of Florida

The 2004 Individuals with Disabilities Education Act led to an era of educational reform that called for scientifically based curriculum and data-driven decision-making when devising instructional strategies. Response to intervention was subsequently endorsed. Because students with disabilities were being included within the general education setting during this time, many states like Florida, made use of the multi-tiered system of supports (MTSS). This process was used to plan and problem-solve effective teaching strategies to improve student performance in reading and in mathematics. This chapter explores how Florida has used MTSS to narrow achievement gaps and create educational opportunities for all students.

Learning Creativity and Students' Performance

This research aims to recognize the influence that creativity units/modules/seminars has on academic performance of students who attend. Consequently, the purpose of this study is to understand the impact that learning creativity has on Porto Accounting and Business School (ISCAP) students. Therefore, a quantitative methodology was developed through a questionnaire with 240 answers from ISCAP's students. The data was studied through reliability and explanatory factorial analysis in order to find the relation between the physical environment, learning climate, and learner engagement on the performance of students. The results show that the physical environment and learning climate have a positive influence on student performance but the same does not happen with the motivation of students. These findings focus on the necessity to improve the student engagement. It is recommended that ISCAP will take into account the factors that lead to the referred results so as to reform and develop new ways in which to improve student performance through student motivation.

Prediction of the Academic Achievement of Pupils Using Data Mining Techniques

Introduction: During this epidemic, a problem in fundamental education affecting all globe is occurring, and we note that education and learning were online and conducted in students. Academic performance of students must be forecast, so that the instructor may better identify the missing pupils and offer teachers a proactive opportunity to develop additional resources for the student to maximize their chances of graduation. Students' academic achievement in higher learning (EH) has been extensively studied in addressing academic inadequacies, rising drop-out rates, graduation delays, and other difficult questions. Simply said, the performance of students refers to the amount to which short and long-term educational objectives are met. Academics nonetheless judge student achievement from different viewpoints, from grades, average grade points (GPAs) to prospective jobs. The literature encompasses numerous computing attempts to improve student performance in schools and colleges, primarily through data mining and analysis learning. However, the efficiency of current smart techniques and models is still unanimous. Method: This study employs multiple methods for machine learning to forecast student progress. With its accurate data sample prediction, five integrated classification algorithms have been created to forecast students' academic success (support vectors, decision-making trees algorithm and perceptron algorithm, logistic regression algorithm and a random forest algorithm). Results: Students' academic achievement has been reviewed and assessed. The performance of five learning machines mentioned in Section 4 is discussed here. First, we displayed the data after pre-processing by simply displaying distributions to form the data packet and then evaluated 5 important learning methods and described the variables in the data set. The entire series of 480 characteristics were examined.

Blended Learning in Principles of Marketing: The Effects of Student Differences on Student Performance

Blended learning, which combines the benefits of both online learning and face-to-face instruction, is becoming popular in higher education. Despite its increase in application, there is limited research on the mechanisms to improve student performance in blended learning courses. This article aims to identify and empirically test individual factors influencing student performance in an introductory marketing course offered in a blended learning format. Through two surveys conducted during the semester at two large, public business schools, we find that metacognitive regulation, metacognitive knowledge, and student effort enhance student performance in blended learning courses. We also find that self-efficacy and course-specific attitudes affect metacognition and student effort. Based on the results, we provide practical strategies to design blended learning courses that improve student performance.

Questioning CRAAP

Librarians and instructors see college students struggle with evaluating information and wonder how to best teach source evaluation in a one-time course integrated library research session to ensure understanding and improve student performance. This research compared multiple sections of first-year students over two semesters taught two evaluation methods: the CRAAP method, and the six journalistic question words. Results indicate that students taught to evaluate information using the six question words produced better end-of-semester papers. Results of the pre-, post-, and end-of-semester quizzes were less conclusive, but do highlight some of the challenges first-year students face when determining credibility. Results have the potential to inform instructional practice.

Flipped Classrooms in Undergraduate Statistics: Online Works Just Fine

Background The flipped classroom method requires that students engage with homework before coming to the classroom so that class time can be spent on active and collaborative learning exercises. Research has demonstrated that this can improve student performance versus traditional lecturer-led teaching methods. Objective: During the COVID-19 pandemic, the vast majority of teaching has been entirely online such that even ‘in-class’ time has been virtual. The current article examined whether online-only delivery affects the efficacy of the flipped classroom approach. Method: Grades for a research methods and statistics module and a statistics portfolio assignment were compared across consecutive cohorts of undergraduate psychology students taught by different methods. Results: Overall grades on the module did not differ significantly across teaching methods but student performance on statistics tests did. Flipped classrooms, whether accompanied by on-campus or synchronous online classes, led to significantly better performance than traditional methods. No detriment was observed by teaching entirely online. Conclusion: The key advantages of the flipped classroom method appear driven by active learning which can occur irrespective of classroom context. Teaching Implications: Using flipped classrooms can be a useful tool, particularly in subjects where students may otherwise be less engaged with the content.

A Learning Analytics-informed Activity to Improve Student Performance in a First Year Physiology Course

Learning Analytics (LA) can be employed to identify course-specific factors that hinder student course (outcome) performance, which can be subsequently rectified using targeted interventions. Supplementing interventions with predictive modelling also permits the identification of students who are at-risk of failing the course and encourages their participation. LA findings suggested that a targeted intervention for our course should focus on improving student short answer question (SAQ) performance, which we attempted to achieve by improving their understanding of features pertaining to various SAQ answer standards and how to achieve them using examples of varying scores. Every student was invited to the intervention via a course-wide announcement through the course learning management system. At-risk students identified using predictive models were given an additional invitation in the form of a personalised email. Results suggest that intervention improved student understanding of SAQ performance criteria. The intervention also enhanced student end-of-semester SAQ performance by 12% and 11% for at-risk and no-risk students respectively. Course failure rate was also lower by 26% and 9% among at-risk and no-risk intervention participants. Student perception of the intervention was also positive where an overwhelming majority of participants (96%) found the interventional activity to be useful for their learning and exam preparations.

Impact of Practicing Mindful Breathing in Class

Many community college students experience high levels of stress due to the demands of coursework and stressors outside of the classroom. Contemplative practices such as deep breathing can reduce stress and increase focus, but students may not be familiar with or feel that they have time to engage in these practices. We hypothesized that introducing students to mindful breathing in our accounting and biology classes would help students manage stress during the semester, reduce test anxiety, and improve student performance. To test this hypothesis, we introduced some sections of our courses to mindful breathing techniques such as square breathing, alternate nostril breathing, and victorious breathing and set aside time at the start of each class to allow students to engage in these practices. At the end of the semester, we surveyed students to assess their past and present experiences with contemplative practices and their perceptions of the benefits of mindful breathing. We also asked students to rate their levels of exam stress and to rate the general feelings of stress they experienced during the semester, comparing their responses with responses from students in control sections that were not introduced to mindful breathing. The survey results indicated that mindful breathing was a new practice to some students and a familiar practice to others. Most students felt that the practice of deep breathing at the start of each class helped them reduce stress, develop a sense of connectedness, and improve focus. However, there was no consistent difference in self-reported exam stress or general stress between students in sections with and without in-class contemplative breathing. There was also no difference in course performance, as reflected by overall course grade, between sections with and without in-class contemplative breathing. Although it was not the focus of this study, we observed that in-class contemplative breathing practice seemed to improve the learning environment and helped build trusting relationships with students.

Implementation of Dual Decision Support System for Students and Teachers using Data Analytics in Online Exam

Online Learning platforms are increasing day by day, and most of the people prefer online learning as this platform is very convenient and affordable. In online learning education, learning content designing plays very important role to improve student performance. Therefore in this project we proposed a data analytics based model to analyze student’s exam solving and content accessing behaviors which will help teachers to improve content quality. Student’s behavior analysis will also help to find out questions difficulty levels as well as student’s grade and performance. We proposed a decision support system in online learning system for tutors/teachers which will help them to improve their learning quality. Along with this the tutors will be able to view student’s performance online using Graphical User Interface. In this system, we proposed a personal questions ordering module for students depending upon their historical question solving patterns.

A Review on Students Performance Prediction using Machine Learning Algorithms

In the current scenario, this is difficult to predict students’ future results based on his/her current performance. As the outcome of this, the teacher can advise him/her to overcome the poor result, and also it can coach the student. By finding out the dependencies for final examinations. The system suggests to students about subject/course selection for the upcoming semester and act as roles of adviser/teacher. Due to improper advice and monitoring a lot of student’s futures in dark. This is difficult for a teacher to analyze and monitors the performance of each and every student. The system can give feedback to teachers about how to improve student performance. This paper carried out a literature review from the year 2003 to 2021. The system predicts his/her future results by applying Machine Learning Algorithms like k- Nearest Neighbor (k-NN), Support Vector Machine (SVM), and Naive Bayes at an earlier stage.