scholarly journals Assessing and Modeling Student Academic Practices and Performance in First-Year Mathematics Courses in Higher Education

2019 ◽  
Vol 9 (2) ◽  
Author(s):  
Kim Ward ◽  
Chantal Larose
Keyword(s):  
Higher Education ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
First Year ◽  
Mathematics Courses ◽  
Course Grades ◽  
Or Practice ◽  
Academic Practices ◽  
Tree Algorithms ◽  
And Performance

Objectives: This research brief explores literature addressing developmental education to identify successful interventions in first-year math courses in higher education. Our goal is to describe the relationship between students’ academic practices and their final course grade in their first-year math courses. Method: Data on 3,249 students have been gathered and analyzed using descriptive statistics and predicative analytics. We describe the Math program, which includes a supplemental support component, and the environment under which it was created. We then examine the behavior between students’ participation in supplemental support and their academic performance. Results: We used classification and regression tree algorithms to obtain a model that gave us data-driven guidelines to aid with future student interventions and success in their first-year math courses. Conclusions: Students’ fulfillment of the supplemental support requirements by specified deadlines is a key predictor of students’ midterm and final course grades.  Implications for Theory and/or Practice: This work provides a roadmap for student interventions and increasing student success with first-year mathematics courses. Keywords: First-year mathematics courses, supplemental support, higher education

scholarly journals Socrative in Higher Education: Game vs. Other Uses

2019 ◽  
Vol 3 (3) ◽  
pp. 49 ◽  
Author(s):  
Fátima Faya Cerqueiro ◽  
Ana Martín-Macho Harrison
Keyword(s):  
Higher Education ◽  
Mobile Application ◽  
Ad Hoc ◽  
Effective Means ◽  
First Year ◽  
Perceived Satisfaction ◽  
And Performance ◽  
Faculty Of Education ◽  
Reading Activity ◽  
First Year University

The integration of clickers in Higher Education settings has proved to be particularly useful for enhancing motivation, engagement and performance; for developing cooperative or collaborative tasks; for checking understanding during the lesson; or even for assessment purposes. This paper explores and exemplifies three uses of Socrative, a mobile application specifically designed as a clicker for the classroom. Socrative was used during three sessions with the same group of first-year University students at a Faculty of Education. One of these sessions—a review lesson—was gamified, whereas the other two—a collaborative reading activity seminar, and a lecture—were not. Ad-hoc questionnaires were distributed after each of them. Results suggest that students welcome the use of clickers and that combining them with gamification strategies may increase students’ perceived satisfaction. The experiences described in this paper show how Socrative is an effective means of providing formative feedback and may actually save time during lessons.

An aggregated and dynamic analysis of innovations in campus sustainability

2018 ◽  
Vol 19 (2) ◽  
pp. 353-375 ◽  
Author(s):  
Camille Washington-Ottombre ◽  
Siiri Bigalke
Keyword(s):  
Higher Education ◽  
Assessment Tool ◽  
Sustainability Assessment ◽  
Regression Tree ◽  
Education Institution ◽  
Classification And Regression Tree ◽  
Internal Factors ◽  
Content Type ◽  
Campus Sustainability

Purpose This paper aims to compose a systematic understanding of campus sustainability innovations and unpack the complex drivers behind the elaboration of specific innovations. More precisely, the authors ask two fundamental questions: What are the topics and modes of implementation of campus sustainability innovations? What are the external and internal factors that drive the development of specific innovations? Design/methodology/approach The authors code and analyze 454 innovations reported within the Sustainability Tracking Assessment and Rating System (STARS), the campus sustainability assessment tool of the Association for the Advancement of Sustainability in Higher Education. Using descriptive statistics and illustrations, the paper assesses the state of environmental innovations (EIs) within STARS. Then, to evaluate the role of internal and external drivers in shaping EIs, the authors have produced classification and regression tree models. Findings The authors’ analysis shows that external and internal factors provide incentives and a favorable context for the implementation of given EIs. External drivers such as climatic zones, local income and poverty rate drive the development of several EIs. Internal drivers beyond the role of the agent of change, often primarily emphasized by past literature, significantly impact the implementation of given EIs. The authors’ work also reveals that EIs often move beyond traditional mitigation approaches and the boundaries of campus. EIs create new dynamics of innovation that echo and reinforce the culture of a higher education institution. Originality/value This work provides the first aggregated picture of EIs in the USA and Canada. It produces a new and integrated understanding of the dynamics of campus sustainability that complexifies narratives and contextualizes the role of change agents.

scholarly journals Personalised Emails in First-Year Mathematics: Exploring a Scalable Strategy for Improving Student Experiences and Outcomes

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Sarah Dart ◽  
Belinda Spratt
Keyword(s):  
Higher Education ◽  
Student Experience ◽  
Student Experiences ◽  
First Year ◽  
Widening Participation ◽  
Mathematics Courses ◽  
Education Environment ◽  
University Systems ◽  
Student Cohorts ◽  
Students First

Widening participation initiatives in higher education have grown overall student numbers while also increasing the diversity of student cohorts. Consequently, enhancing student experiences and outcomes has become increasingly challenging. This study implemented personalised emails in two first-year mathematics courses as a scalable strategy for supporting students with diverse needs. Impact on student experience and outcomes was assessed through surveying and statistical comparisons to previous cohorts. It was found that students perceived the personalised emails favourably and believed the intervention would contribute to them achieving better grades. This translated to a statistically significant improvement in both student experience and academic performance in one of the courses. The results imply that personalised emails are well-suited to courses taken in students’ first semesters of university study, aiding those transitioning to the higher education environment by fostering feelings of belonging, supporting effective engagement, and easing navigation of university systems and processes.

scholarly journals Student’s access and performance in the Portuguese Higher Education: Issues of gender, age, socio-cultural background, expectations, and program choice

2019 ◽  
Vol 24 (2) ◽  
pp. 434-450
Author(s):  
Maria Eugénia Ferrão ◽  
Leandro S. Almeida
Keyword(s):  
Higher Education ◽  
Multivariate Analysis ◽  
Analysis Of Variance ◽  
Cultural Background ◽  
First Year ◽  
Transfer Program ◽  
Multivariate Analysis Of Variance ◽  
Grade Point ◽  
And Performance ◽  
Portuguese Higher Education

The purpose of this article is to characterize and contribute to the debate on the democratization of Portuguese higher education, both in terms of access and the performance of students enrolled in a public university. The analyses concern the sociodemographic characteristics and schooling trajectory of the 2,697 students enrolled for the first time in the University of Minho in the academic year 2015/16. The relationships between such characteristics and the choice of program, expectations regarding higher education, the criteria of admission, and the association with their permanence and performance in the first year of studies are explored as well. Several statistical tests were applied, such as those based on multivariate analysis of variance, chi-squared test for the independence between variables, or the t-Student test for the comparison of means of two independent samples. Results suggest that student’s gender, socio-cultural background and schooling trajectory are related to the choice of the programe, university entrance score and the entrance option. The multivariate analysis of variance of student’s grade point average at the end of the first year suggests the influence of the interaction between the fixed term of scientific-disciplinary area of the program attended and the program option of access to higher education. We did not find any statistically significant association between socio-cultural background and permanence in higher education; i.e, the socio-cultural origin of the students does not seem to influence the decision to abandon, suspend or transfer program, at least during their first year of studies. Our findings suggest student’s resilience and/or institutional action meaning a step further on the path for social equity in the Portuguese higher education.

scholarly journals Modelling and Prediction of Concrete Compressive Strength Using Machine Learning

2021 ◽  
pp. 526-532
Author(s):  
K Sumanth Reddy ◽  
Gaddam Pranith ◽  
Karre Varun ◽  
Thipparthy Surya Sai Teja
Keyword(s):  
Machine Learning ◽  
Compressive Strength ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Rational Relation ◽  
Compressive Strength Of Concrete ◽  
And Performance ◽  
Materials Used

The compressive strength of concrete plays an important role in determining the durability and performance of concrete. Due to rapid growth in material engineering finalizing an appropriate proportion for the mix of concrete to obtain the desired compressive strength of concrete has become cumbersome and a laborious task further the problem becomes more complex to obtain a rational relation between the concrete materials used to the strength obtained. The development in computational methods can be used to obtain a rational relation between the materials used and the compressive strength using machine learning techniques which reduces the influence of outliers and all unwanted variables influence in the determination of compressive strength. In this paper basic machine learning technics Multilayer perceptron neural network (MLP), Support Vector Machines (SVM), linear regressions (LR) and Classification and Regression Tree (CART), have been used to develop a model for determining the compressive strength for two different set of data (ingredients). Among all technics used the SVM provides a better results in comparison to other, but comprehensively the SVM cannot be a universal model because many recent literatures have proved that such models need more data and also the dynamicity of the attributes involved play an important role in determining the efficacy of the model.

scholarly journals Decision Trees for Evaluation of Mathematical Competencies in the Higher Education: A Case Study

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 748 ◽  
Author(s):  
Atanas Ivanov
Keyword(s):  
Higher Education ◽  
Classification Tree ◽  
Regression Tree ◽  
Educational Process ◽  
Classification And Regression Tree ◽  
Choice Test ◽  
Knowledge And Skills ◽  
Mathematical Competencies ◽  
Tree Models

The assessment of knowledge and skills acquired by the student at each academic stage is crucial for every educational process. This paper proposes and tests an approach based on a structured assessment test for mathematical competencies in higher education and methods for statistical evaluation of the test. A case study is presented for the assessment of knowledge and skills for solving linear algebra and analytic geometry problems by first-year university students. The test includes three main parts—a multiple-choice test with four selectable answers, a solution of two problems with and without the use of specialized mathematical software, and a survey with four questions for each problem. The processing of data is performed mainly by the classification and regression tree (CART) method. Comparative analysis, cross-tables, and reliability statistics were also used. Regression tree models are built to assess the achievements of students and classification tree models for competency assessment on a three-categorical scale. The influence of 31 variables and groups of them on the assessment of achievements and grading of competencies is determined. Regression models show over 94% fit with data and classification ones—up to 92% correct classifications. The models can be used to predict students’ grades and assess their mathematical competency.

Online Homework and Correlated Success in University Mathematics Courses

2014 ◽  
pp. 307-329 ◽  
Author(s):  
Stephen W. Kuhn ◽  
Sandy W. Watson ◽  
Terry J. Walters
Keyword(s):  
First Year ◽  
Online Homework ◽  
University Of Kentucky ◽  
Web Based ◽  
Mathematics Courses ◽  
University Of Tennessee ◽  
University Mathematics ◽  
Course Grades ◽  
The University ◽  
The Web

The primary goals of this project at the University of Tennessee at Chattanooga (UTC) were to use a free, open-source online tool developed at the University of Kentucky (UK) called WHS (Web Homework System) for Web-based homework and quizzes in first year mathematics courses and to demonstrate that the use of this system by students would improve and correlate well with their success in these courses. Quantitative data were collected and analyzed across four years involving 832 students using this system and 753 not using the system in seven courses. The findings indicate that faculty and students found the Web-based homework assignments helpful for a variety of reasons, though some of each found it occasionally frustrating. Students with high (low) scores on the Web homework had a very high probability of having high (low) grades in the courses, but there were no statistically significant improvements in final course grades over traditional methods.

Prediction intervals with random forests

2019 ◽  
Vol 29 (1) ◽  
pp. 205-229 ◽  
Author(s):  
Marie-Hélène Roy ◽  
Denis Larocque
Keyword(s):  
Random Forests ◽  
Prediction Interval ◽  
Regression Tree ◽  
Real Data ◽  
Calibration Procedure ◽  
Prediction Intervals ◽  
Classification And Regression Tree ◽  
Alternative Methods ◽  
Functional Link ◽  
And Performance

The classical and most commonly used approach to building prediction intervals is the parametric approach. However, its main drawback is that its validity and performance highly depend on the assumed functional link between the covariates and the response. This research investigates new methods that improve the performance of prediction intervals with random forests. Two aspects are explored: The method used to build the forest and the method used to build the prediction interval. Four methods to build the forest are investigated, three from the classification and regression tree (CART) paradigm and the transformation forest method. For CART forests, in addition to the default least-squares splitting rule, two alternative splitting criteria are investigated. We also present and evaluate the performance of five flexible methods for constructing prediction intervals. This yields 20 distinct method variations. To reliably attain the desired confidence level, we include a calibration procedure performed on the out-of-bag information provided by the forest. The 20 method variations are thoroughly investigated, and compared to five alternative methods through simulation studies and in real data settings. The results show that the proposed methods are very competitive. They outperform commonly used methods in both in simulation settings and with real data.

C_CART: An instance confidence-based decision tree algorithm for classification

2021 ◽  
Vol 25 (4) ◽  
pp. 929-948
Author(s):  
Shuang Yu ◽  
Xiongfei Li ◽  
Hancheng Wang ◽  
Xiaoli Zhang ◽  
Shiping Chen
Keyword(s):  
Decision Tree ◽  
Simple Structure ◽  
Regression Tree ◽  
Classification Performance ◽  
Classification And Regression Tree ◽  
Decision Tree Algorithm ◽  
Tree Algorithms ◽  
Degree Of Confidence ◽  
Cart Algorithm ◽  
The Impact

In classification, a decision tree is a common model due to its simple structure and easy understanding. Most of decision tree algorithms assume all instances in a dataset have the same degree of confidence, so they use the same generation and pruning strategies for all training instances. In fact, the instances with greater degree of confidence are more useful than the ones with lower degree of confidence in the same dataset. Therefore, the instances should be treated discriminately according to their corresponding confidence degrees when training classifiers. In this paper, we investigate the impact and significance of degree of confidence of instances on the classification performance of decision tree algorithms, taking the classification and regression tree (CART) algorithm as an example. First, the degree of confidence of instances is quantified from a statistical perspective. Then, a developed CART algorithm named C_CART is proposed by introducing the confidence of instances into the generation and pruning processes of CART algorithm. Finally, we conduct experiments to evaluate the performance of C_CART algorithm. The experimental results show that our C_CART algorithm can significantly improve the generalization performance as well as avoiding the over-fitting problem to a certain extend.

Relationship of stress and performance among first-year dental students

1986 ◽  
Vol 50 (5) ◽  
pp. 264-267 ◽  
Author(s):  
GH Westerman ◽  
TG Grandy ◽  
JV Lupo ◽  
RE Mitchell
Keyword(s):  
Dental Students ◽  
First Year ◽  
And Performance ◽  
Relationship Of
Sign in / Sign up

Export Citation Format

Share Document