Factors of Student Success

The purpose of my study is to understand student success in college. I focused a large amount of my research around extracurricular activities out of the classroom, which include clubs, sports, faculty interaction, employment, and time spent on preparation for classes. In order to understand student success, I sent out a survey to 200 students enrolled in Augustana to evaluate their responses to see what they spent their time doing. I paid close attention to the number of hours students spend on various activities each day. The results allowed me to understand student habits, and how their involvement in extracurriculars, homework, and social environments impacted grade point averages (GPA)

Development of a system architecture for the prediction of student success using machine learning techniques

“ The goals of higher education have evolved through time based on the impact that technology development and industry have on productivity. Nowadays, jobs demand increased technical skills, and the supply of prepared personnel to assume those jobs is insufficient. The system of higher education needs to evaluate their practices to realize the potential of cultivating an educated and technically skilled workforce. Currently, completion rates at universities are too low to accomplish the aim of closing the workforce gap. Recent reports indicate that 40 percent of freshman at four-year public colleges will not graduate, and rates of completion are even lower for community colleges. Some efforts have been made to adjust admission requirements and develop systems of support for different segments of students; however, completion rates are still considered low. Therefore, new strategies need to consider student success as part of the institutional culture based on the information technology support. Also, it is key that the models that evaluate student success can be scalable to other higher education institutions. In recent years machine learning techniques have proven to be effective for such purpose. Then, the primary objective of this research is to develop an integrated system that allows for the application of machine learning for student success prediction. The proposed system was evaluated to determine the accuracy of student success predictions using several machine learning techniques such as decision trees, neural networks, support vector machines, and random forest. The research outcomes offer an important understanding about how to develop a more efficient and responsive system to support students to complete their educational goals”--Abstract, page iv

Predicting students' academic achievement using methods of educational data mining

The tremendous growth in educational data forms the need to have meaningful information produced from it. Educational Data Mining (EDM) has become an exciting research area that can reveal valuable knowledge from educational databases. This knowledge can be used for many purposes, including identifying dropouts or weak students who need special attention and discovering extraordinary students who can be presented with lifetime opportunities. This thesis allows the reader to grasp the field of EDM from all its angles, with more details on academic prediction tasks. It provides a comprehensive background for understanding EDM and discusses the different methods and applications of data mining in education. It also provides a rich literature review on predicting students’ academic achievement and covers related works from 2007 to 2022. Furthermore, it examines the application of machine learning algorithms to predict students’ academic achievement on two diverse datasets. The first dataset has been obtained from the Computer and Information Science College at Princess Norah University (PNU) in Riyadh, Saudi Arabia. In this work, 300 undergraduate students’ records have been used to predict their final academic achievement. We used the Weka software to compare the performance of eight data mining algorithms in predicting students’ academic achievement. Those algorithms are C4.5, Simple CART, LADTree, Support Vector Machine, Naïve Bayes, K-nearest-Neighbor, Artificial Neural Networks, and Random Forest and validated the models using 10-folds cross-validation. The empirical results show that: (i) In the College of Computer and Information Science, the following features are the most essential to predict student academic achievement: the student GPA in each semester, the number of failed courses during the first four semesters, and the grades of three core courses. On the other hand, student's proficiency in English and the number of registered credit hours do not play a major role in their success (ii) Naïve Base performs the best in predicting students’ achievement followed by Random Forest; (iii) Students who attend an orientation year do not have a greater chance of success at that college. The second dataset represents the records of the Business Informatics master's students at the University of Mannheim in Germany. In this work, more than 700 undergraduate students’ data have been used to predict their final academic achievement using different machine learning libraries in python. We compared the performance of nine data mining algorithms in predicting students’ academic achievement. Those algorithms are Logistic Regression, Naïve Bayes, K-nearest neighbor, Artificial Neural Networks, Support Vector Machine, Random Forest, Gradient Boosting, Light Gradient Boosting, and Extreme Gradient Boosting and validated the models using 10-folds cross-validation. The empirical results show the following: (i) Bagging and Boosting algorithms produce a better predictive performance as compared to individual classifiers, and (ii) the semesters’ grades are the most significant features for the predictive model, followed by students’ culture and distance from students’ accommodation to university campus. The outcomes of the two studies can be used to design a recommender system that enables timely interventions for the undergraduate students of the College of Information and Computer Science and the postgraduate students of the Business Informatics program

European tourist perspective on destination satisfaction: a business analytics approach

For many years that tourism information has been collected and stored, allowing increased interest in the data mining (DM) areas. This leads to a need of research and discovery of new patterns to develop automated procedures to improve the tourism knowledge management. The relationship between the tourist characteristics and preferences and the tourist satisfaction was never studied in order to provide useful knowledge to the tourism industry. Therefore, there was the need to investigate the explanatory factors of the tourist satisfaction with the destination to allow the tourism companies to define the correct assumptions about a certain travel. This dissertation used the data from Flash Eurobarometer 414 “Preferences of Europeans towards tourism 2015” with data from the 28 countries of the European Union (EU). A predictive model was obtained for the tourist satisfaction, through the discovery of existing patterns in the process of the tourist travel, using DM techniques on the data referred above. The definition of an explanatory model allowed to obtain useful knowledge for tourism agencies, enabling the development of marketing strategies according to the tourist profile and ensuring development of promotional messages for products and experiences, ensuring that correct assumptions are made about their customers.Desde há muito tempo que é recolhida e armazenada informação sobre turismo, permitindo captar o interesse das áreas de data mining (DM). Consequentemente, surgiu a necessidade de pesquisa e descoberta de novos padrões para desenvolver procedimentos automatizados, de forma a melhorar a gestão deste tipo de informação. A relação entre as características do turista, as suas preferências e a satisfação nunca foram estudadas extensivamente de forma a criar conhecimento útil para a indústria do turismo. Desta forma, havia a necessidade de investigar e estudar os fatores explicativos da satisfação do turista com o destino, para que seja possível às empresas de turismo traçar o perfil de turista adequado e transmitir as campanhas de marketing de forma assertiva e eficiente. Nesta dissertação foram utilizados os dados do Flash Eurobarometer 414 “Preferences of Europeans towards tourism 2015”, que contém dados dos 28 países da União Europeia. Através da descoberta de padrões existentes no processo de viagem do turista, utilizando técnicas de DM sobre os dados acima referidos, foi possível obter um modelo preditivo para a satisfação do turista. A definição de um modelo explicativo permitiu obter conhecimento útil para as empresas de turismo, facilitando o desenvolvimento de estratégias de marketing de acordo com o perfil do turista e de mensagens promocionais para produtos e experiências, garantindo que são definidos pressupostos adequados para os seus clientes

Futures Studies in the Interactive Society

This book consists of papers which were prepared within the framework of the research project (No. T 048539) entitled Futures Studies in the Interactive Society (project leader: Éva Hideg) and funded by the Hungarian Scientific Research Fund (OTKA) between 2005 and 2009. Some discuss the theoretical and methodological questions of futures studies and foresight; others present new approaches to or procedures of certain questions which are very important and topical from the perspective of forecast and foresight practice. Each study was conducted in pursuit of improvement in futures fields

A case study on the relationship between factors of graduate student engagement and academic achievement at a historically Black college and university (HBCU), 2016

The purpose of the mixed method study was to examine the relationship between factors of graduate student engagement and academic achievement at a historically black college and university (HBCU). The independent variables were graduate student safety in the learning environment, graduate student to graduate student relationships, graduate student to faculty relationships, graduate students self-efficacy, graduate students motivation, graduate students faculty mentoring, graduate students integration, graduate students study habits, and graduate students use of technology. Qualitatively, correlational research was used to examine the extent of the relationship between independent variables and academic achievement. Qualitatively, the phenomenological approach was used to investigate graduate student perceptions of engagement factors and academic achievement. The mixed method helped analyze the convergence between qualitative and quantitative data. Miller and Cameron (2011) found that the mixed method of research has been used widely and accepted in the field of Education. The quantitative data were collected from 209 graduate students. The data content validity was checked with the Pearson r 2-tailed correlation. The Pearson Correlation helped to test for a significant relationship between variables. Qualitative data were collected from the interviews of two graduate students from four different graduate departments equaling eight interview participants. One focus group with a minimum of three graduates was conducted from four different departments. A total of 16 graduate students participated in the focus groups. The researcher interpreted the statements from the interviews and focus groups and conducted a document analysis revealing codes and themes that were organized into an analysis matrix. The findings revealed that there was a significant relationship between graduate students safety in the learning environment and academic achievement. There was a significant relationship between graduate student to student relationships and academic achievement. There was a significant relationship between (a) graduate student to faculty relationships and academic achievement, (b) graduate students self-efficacy and academic achievement, (c) graduate students motivation and academic achievement, (d) graduate students faculty mentoring and academic achievement, (e) graduate students integration and academic achievement, (f) graduate students study habits and academic achievement, and (g) graduate students use of technology and academic achievement. KEY TERMS: Student Engagement and Academic Achievement, Educational Administration and Supervision, Educational Leadershi

Aportes de la prospectiva al desarrollo de modelos de enseñanza en la modalidad blended learning para carreras de Ingeniería

Se presentan los resultados de un estudio cuali cuantitativo, con orientación prospectiva, con el objetivo de identificar variables clave para implementar un modelo educativo en la modalidad Blended Learning que favorezca la percepción de los alumnos respecto de su rendimiento académico. Se presentan los resultados del análisis estructural realizado así como también el análisis de juego de actores, los que permiten visualizar las variables estratégicas del sistema definido,- en este caso de carreras de ingeniería,- como así también el posicionamiento de los diferentes actores respecto de los posibles apoyos que eventualmente podrían dar a las acciones que se implementen en el marco de un modelo de enseñanza innovado

What factors influence whether politicians’ tweets are retweeted? Using CHAID to build an explanatory model of the retweeting of politicians’ tweets during the 2015 UK General Election campaign

Twitter is ever-present in British political life and many politicians use it as part of their campaign strategies. However, little is known about whether their tweets engage people, for example by being retweeted. This research addresses that gap, examining tweets sent by MPs during the 2015 UK General Election campaign to identify which were retweeted and why. A conceptual model proposes three factors which are most likely to influence retweets: the characteristics of (1) the tweet’s sender, (2) the tweet and (3) its recipients. This research focuses on the first two of these. Content and sentiment analysis are used to develop a typology of the politicians’ tweets, followed by CHAID analysis to identify the factors that best predict which tweets are retweeted. The research shows that the characteristics of tweet and its sender do influence whether the tweet is retweeted. Of the sender’s characteristics, number of followers is the most important – more followers leads to more retweets. Of the tweet characteristics, the tweet’s sentiment is the most influential. Negative tweets are retweeted more than positive or neutral tweets. Tweets attacking opponents or using fear appeals are also highly likely to be retweeted. The research makes a methodological contribution by demonstrating how CHAID models can be used to accurately predict retweets. This method has not been used to predict retweets before and has broad application to other contexts. The research also contributes to our understanding of how politicians and the public interact on Twitter, an area little studied to date, and proposes some practical recommendations regarding how MPs can improve the effectiveness of their Twitter campaigning. The finding that negative tweets are more likely to be retweeted also contributes to the ongoing debate regarding whether people are more likely to pass on positive or negative information online

Determinación de factores estratégicos en la implementación de un modelo blended learning en carreras de Ingeniería orientado a mejorar los indicadores académicos

Se presentan los resultados de un estudio prospectivo que se orienta a identificar factores clave e intereses de actores en un proceso de implementación de un modelo educativo que integre Tecnologías de Comunicación e Información a la enseñanza de la Ingeniería.Eje: Educación en Tecnología.Red de Universidades con Carreras en Informática (RedUNCI

Curriculum development for an inquiry approach to construction education.

Doctoral Degree. University of KwaZulu-Natal, Durban.University graduates have been criticised for failing to make a meaningful contribution to professional practice in the construction industry in South Africa and across the world generally. Deficiencies have been reported in the ability of graduates of construction programmes to think critically, solve problems or apply theoretical knowledge in practical situations. Among other factors, the traditional didactic lecture approach to teaching and learning has been blamed for not providing students with an appropriate learning experience to adequately prepare them for professional practice. This is because the didactic lecture approach is characterised by attempts to transmit knowledge from the lecturer to the student which has been found to be inadequate in achieving effective learning. The traditional didactic approach to teaching is based on theories of learning which assumed that knowledge can be transmitted from the minds of lecturers to the minds of students. Contemporary theories of learning have rebuffed this assumption and demonstrated that knowledge and understanding are achieved by students actively engaging with the study material and constructing their own knowledge structures rather than passively receiving knowledge and understanding. Based on these contemporary theories of learning, several different pedagogy has been suggested and incorporated in educational practice. However, predominantly, contemporary pedagogy has been haphazardly applied within the traditional framework of segregated modules. Also, different pedagogy based on different contemporary theories has been researched and applied independent of each other. This has led to some contradictions in some pedagogy and a lack of synergistic collaboration among the contemporary pedagogy. Against this background, this thesis researched the problem that the traditional didactic lecture teaching approach to construction education at undergraduate level does not adequately prepare students for construction professional practice and therefore requires an alternative curriculum model which incorporates different contemporary theories of learning synergistically in a student centred inquiry based learning (IBL) pedagogical framework. To achieve this, the research established factors from the contemporary theories of learning which significantly contribute to the creation of knowledge structures in students studying construction programmes in South Africa. Subsequently the research proposed a curriculum model for construction programmes which incorporated the identified antecedents to effective learning underpinned in the contemporary pedagogical framework of IBL. The research followed a positivist epistemological philosophy and a subjective ontological philosophy, a deductive research approach, a survey research strategy, a cross sectional time horizon and a data collection technique and procedure of a questionnaire using the non-probability sampling technique of convenient sampling. The research procedure included an extensive literature review of three contemporary theories of learning namely, constructivism from philosophy, connectionism from behaviourism and cognitive load theory from cognitive science. Subsequently, an instrument measuring the concepts from the conceptual model was developed, pre-tested and then administered to undergraduate students studying construction programmes at a convenient sample of public universities in South Africa. The results show that the factors from the three contemporary theories of learning which directly influence the extent to which students studying construction programmes are able to create knowledge structures and achieve effective learning are individual learning, scaffolding, reflective thinking and group learning in that order. Repetition, reinforcement, readiness, self-directed learning and the use of worked examples have indirect relationships with the ability for students to create knowledge structures. Complex questions and authentic questions were also found to indirectly contribute to effective learning. Cognitive loading was found to interfere with learning and complex questions were found to induce cognitive loading while authentic questions did not. Subsequently, an IBL curriculum framework for construction programmes was proposed which integrated most of the topics which directly relate to construction practice. Based on the findings, the IBL class should involve students in both individual and group learning activities which should be appropriately scaffolded and students explicitly directed towards reflective thinking as they engage in the IBL projects. Complex questions and authentic questions should be used in collaboration with extra scaffolding in order to reduce the impact of the consequent cognitive loading induced by complex questions. The IBL projects should be simple initially and increase in complexity as the student’s advance