Degree of Scaffolding: Learning Objective Metadata: A Prototype Leaning System Design for Integrating GIS into a Civil Engineering Curriculum

Digital media and networking offer great potential as tools for enhancing classroom learning environments, both local and distant. One concept and related technological tool that can facilitate the effective application and distribution of digital educational resources is learning objects in combination with the SCORM (sharable content objects reference model) compliance framework. Progressive scaffolding is a learning design approach for educational systems that provides flexible guidance to students. We are in the process of utilizing this approach within a SCORM framework in the form of a multi-level instructional design. The associated metadata required by SCORM will describe the degree of scaffolding. This paper will discuss progressive scaffolding as it relates to SCORM compliant learning objects, within the context of the design of an application for integrating Geographic Information Systems (GIS) into the civil engineering curriculum at the University of Missouri - Rolla

Community college selective enrollment and the challenge to open access

The open access mission is central to the community college role and mission in higher education. Although initially implemented by four-year colleges and universities, adoption of formal enrollment management initiatives in community colleges is on the increase. Admission, matriculation, retention, and persistence are affected by enrollment management policies. Initiatives designed to control enrollment may alter the open access commitment of the community college by limiting access to some students. Enrollment management practices at the community college can include selective marketing and recruiting practices. This study examined the prevalence of selective marketing and recruiting practices at North Carolina community colleges and the impact of such practices on enrollment. Results of the study indicated that about half of the community colleges in North Carolina practice selective marketing and recruiting practices, although to date those practices have had no apparent impact on the demographic composition of the student body. Student demographic representation in enrollment at North Carolina community colleges was statistically significantly different than the corresponding demographic composition of college service areas. Organizational depth of marketing implementation at selective colleges was compared to the demographic composition of student body enrollment. There was no relationship between organizational depth of marketing implementation at selective colleges and student body demographic composition. Study results inform decisions affecting the use of selective marketing and recruiting practices within the context of the open access mission at the community college. Implications for policy and practice include the recommendation to create an enrollment management division at each community college, to streamline use of the marketing dollar, and to increase the use of marketing to influence the decision-making process of internal stakeholders

The Association between Success Center Utilization and a Technical College’s Student Retention

This study was conducted to examine the association between the Student Success Center and student retention at a South Carolina technical college. Recognizing the low retention rates of technical colleges in South Carolina and nationally, the college opened a Student Success Center in 2012; however, an analysis of the center’s effect on retention rates had not been conducted. With a better understanding of this relationship, the college can plan for future use of the center to strengthen retention. The key research question was focused on the association between Student Success Center attendance and student retention using an ex post facto design involving two dichotomous variables: attendance at the Student Success Center and retention over 3 years. A sample of 18,712 students was drawn from archival data maintained by the college to compare students who used the center and those who did not use the center, excluding transfer students and middle college students. Frequency percentage statistics were generated for the two dichotomous categorical variables in the study: center utilization and retention. Chi-square analysis with Yates correction was used to test for a significant association between the two variables. Findings showed evidence of a statistically significant association between center utilization and retention, χ2 (1) = 162.23, p \u3c 0.0001, indicating that student engagement with the Student Success Center contributed to resiliency as reflected in student retention. Therefore, this study contributed to research on the association between student support services for community college students and student retention, encouraging social change by strengthening practical solutions to the challenges faced by these students

Big data in education and organizational change: Evidence from private K12 schools in China

China is a time-honored civilization with a long history of private education. In China, private education has played an important role in preserving Chinese civilization. At the end of the 20th century, private education in China began to develop thanks to government support. As such, remarkable progress was made during the past decade. Due to specific conditions within the education industry, however, the administration of private edu-cation - and basic education, in particular - has remained rudimentary compared with other more mature service industries. To address the many problems in basic education, such as rig-id teaching methods, heavy teacher workloads and long, repetitive working hours, it is imper-ative in this information era to conduct innovative explorations with the help of the “internet of things” (IoT), big data and other scientific and technological means to carry out organiza-tional reform in schools and to establish contemporary organizational structures and manage-ment modes. Doing so will comprehensively improve the administration of basic education, which will in turn promote the quality of education and teaching. This thesis examines Tianli Education Group, a typical example of private, basic educa-tion in China. By adopting experimental research methods, the behavior of students and teachers in Tianli’s schools were experimentally analyzed. IoT technology was employed to collect data about student behavior at school. Likewise, after collecting and analyzing big data on the behavior of teachers at school, the content and processes of their work were analyzed. Based on these experiments, this thesis explores a new 5G era-appropriate mode of stu-dent selection and training that makes use of big data technology. It outlines the standard work scenario for teachers and improves both their work efficiency and salaries by “trimming staff and streamlining administration,” thus rekindling enthusiasm among teachers for their work. Finally, as a part of this thesis, a series of organizational changes were implemented at Tianli Education Group and its schools to boost organizational vitality, improve overall levels of education, teaching and operational efficiency, raise teachers’ salaries and enhance student happiness.A China é uma civilização muito antiga, com uma longa história de educação privada. A educação privada desempenhou um papel importante na preservação da civilização chinesa. No final do século 20, a educação privada na China começou a desenvolver-se com o apoio do governo. Nos últimos dez anos, devido ao apoio concedido temos assistido a um grande progresso. Contudo e em virtude das condições específicas da indústria da educação, a administração da educação privada – a educação básica em particular – permaneceu rudimentar quando comparada com outras indústrias de serviços. Para resolver os muitos problemas da educação básica, tais como os métodos rígidos de ensino, as cargas de trabalho pesadas e horas de trabalho repetitivas, torna-se imperativo nesta era da informação realizar pesquisas inovadoras com a ajuda da “Internet das Coisas”, do “Big Data” e meios científicos e tecnológicos que nos permitam realizar a reforma nas escolas e estabelecer estruturas organizacionais e métodos de gestão adaptados aos tempos em que vivemos. Os resultados destas pesquisas irão contribuir para melhorar de uma forma abrangente a administração da educação básica, o que por sua vez promoverá a qualidade da educação e do ensino. Esta tese estuda o Tianli Education Group, que consideramos um bom exemplo do ensino privado na educação básica na China. Adoptando métodos experimentais de pesquisa, o comportamento dos estudantes e professores das escolas Tianli foram analisados. Aplicamos a tecnologia da “Internet das Coisas” para recolher informações sobre comportamento dos alunos na escola. Da mesma forma, após a recolha e análise dos dados sobre o comportamento dos professores na escola, efetuamos a análise do conteúdo e dos processos do seu trabalho. Tendo por base estas experiências, esta tese explora na nova era 5G, um modo apropriado para seleção e formação dos alunos. Esta tese descreve o cenário padrão de trabalho para professores e melhora não somente a eficiência do trabalho como também os seus salários ao “reduzir o pessoal e simplificar a administração”, reacendendo assim o entusiasmo dos professores pelo seu trabalho. Finalmente, como parte desta tese, uma série de mudanças organizacionais foram implementadas nas escolas do grupo Tianli Education Group com a finalidade de impulsionar a vitalidade organizacional, melhorar todos os níveis gerais de educação, aumentar a eficiência operacional e de ensino, aumentar os salários dos professores e aumentar a felicidade dos alunos

Student Modeling From Different Aspects

With the wide usage of online tutoring systems, researchers become interested in mining data from logged files of these systems, so as to get better understanding of students. Varieties of aspects of students’ learning have become focus of studies, such as modeling students’ mastery status and affects. On the other hand, Randomized Controlled Trial (RCT), which is an unbiased method for getting insights of education, finds its way in Intelligent Tutoring System. Firstly, people are curious about what kind of settings would work better. Secondly, such a tutoring system, with lots of students and teachers using it, provides an opportunity for building a RCT infrastructure underlying the system. With the increasing interest in Data mining and RCTs, the thesis focuses on these two aspects. In the first part, we focus on analyzing and mining data from ASSISTments, an online tutoring system run by a team in Worcester Polytechnic Institute. Through the data, we try to answer several questions from different aspects of students learning. The first question we try to answer is what matters more to student modeling, skill information or student information. The second question is whether it is necessary to model students’ learning at different opportunity count. The third question is about the benefits of using partial credit, rather than binary credit as measurement of students’ learning in RCTs. The fourth question focuses on the amount that students spent Wheel Spinning in the tutoring system. The fifth questions studies the tradeoff between the mastery threshold and the time spent in the tutoring system. By answering the five questions, we both propose machine learning methodology that can be applied in educational data mining, and present findings from analyzing and mining the data. In the second part, we focused on RCTs within ASSISTments. Firstly, we looked at a pilot study of reassessment and relearning, which suggested a better system setting to improve students’ robust learning. Secondly, we proposed the idea to build an infrastructure of learning within ASSISTments, which provides the opportunities to improve the whole educational environment

LABRAD : Vol 46, Issue 4 - October 2021

Role of Barcoding in a Clinical Laboratory to Reduce Pre-Analytical Errors Congenital Dyserythropoietic Anemia: The Morphological Diagnosis Digital Imaging in Hematology: A New Beginning Metabolomics: Identification of Fatty Acid Oxidation (FAO) Disorders Next-Generation Sequencing for HLA Genotyping Urine Metabolomics to identify Organic Academia Next-Generation Sequencing (NGS) of Solid Tumor Importance of using Genomic Tool in Microbial Identification Radiology Practice in 21st Century: Role of Artificial Intelligence Case Quiz Best of the Recent Past Polaroidhttps://ecommons.aku.edu/labrad/1036/thumbnail.jp

The effectiveness of using intelligent tutoring systems to increase student achievement

Intelligent Tutoring Systems could be used to provide differentiated instruction. This review examines qualities of Intelligent Tutoring Systems and their impact on student achievement. Thirty peer-reviewed research studies published from 1997 to 2019 were selected for analysis. This review considers how intelligent tutoring systems compare with other methods of instruction, and how an intelligent tutoring system’s on-screen tutor impacts student achievement. Finally, this review considers methods of ITS personalization and how those methods impact student achievement. The reviewed research studies indicated that ITS was more effective than all forms of instruction except small group and individualized instruction. Additionally, on-screen agents in and personalization of Intelligent Tutoring Systems often have a positive impact on student learning. Recommendations for classroom implementation of intelligent tutoring systems and suggestions for future research are discusse

Ideological Misalignment in the Discourse(s) of Higher Education: Comparing University Mission Statements with Texts from Commercial Learning Analytics Providers

This study analyzes, interprets, and compares texts from different educational discourses. Using the Critical Discourse Analysis method, I reveal how texts from university mission statements and from commercial learning analytics providers communicate and construct different ideologies. To support this analysis, I explore literature strands related to public higher education in America and the emerging field of study and practice called learning analytics. Learning analytics is the administrative, research, and instructional use of large sets of digital data that are associated with and generated by students. The data in question may be generated by incidental online activity, and it may be correlated with a host of other data related to student demographics or academic performance. The intention behind educational data systems is to find ways to use data to “optimize” instructional materials and practices by tailoring them to perceived student needs and behaviors, and to trigger “interventions” ranging from warning messages to prescribed courses of study. The use of data in this way raises questions about how such practices relate to the goals and ideals of higher education, especially as these data systems employ similar theories and techniques as those used by corporate juggernauts such as Facebook and Google. Questions not only related to privacy and ownership but also related to how learning, education, and the purpose of higher education are characterized, discussed, and defined in various discourses are explored in this study

Tutoring Students with Adaptive Strategies

Adaptive learning is a crucial part in intelligent tutoring systems. It provides students with appropriate tutoring interventions, based on students’ characteristics, status, and other related features, in order to optimize their learning outcomes. It is required to determine students’ knowledge level or learning progress, based on which it then uses proper techniques to choose the optimal interventions. In this dissertation work, I focus on these aspects related to the process in adaptive learning: student modeling, k-armed bandits, and contextual bandits. Student modeling. The main objective of student modeling is to develop cognitive models of students, including modeling content skills and knowledge about learning. In this work, we investigate the effect of prerequisite skill in predicting students’ knowledge in post skills, and we make use of the prerequisite performance in different student models. As a result, this makes them superior to traditional models. K-armed bandits. We apply k-armed bandit algorithms to personalize interventions for students, to optimize their learning outcomes. Due to the lack of diverse interventions and small difference of intervention effectiveness in educational experiments, we also propose a simple selection strategy, and compare it with several k-armed bandit algorithms. Contextual bandits. In contextual bandit problem, additional side information, also called context, can be used to determine which action to select. First, we construct a feature evaluation mechanism, which determines which feature to be combined with bandits. Second, we propose a new decision tree algorithm, which is capable of detecting aptitude treatment effect for students. Third, with combined bandits with the decision tree, we apply the contextual bandits to make personalization in two different types of data, simulated data and real experimental data

Measuring Complex Problem Solving in Jordanian Higher Education: Feasibility, Construct Validity and Logfile-Based Behavioural Pattern Analyses

This study investigated the feasibility and the applicability of using the third generation computer-based assessment of complex problem solving (CPS) based on the MicroDYN approach at the Jordanian higher education level. The tests were administered online via the eDia assessment platform for all data collections processes. We also investigated the role of strategic exploration and different problem-solving and test-taking behaviours in CPS success, using logfile data to visualize and quantify students’ problem-solving behaviour on ten CPS problems with varying difficulty levels and characteristics. Additionally, in the present study, we go beyond the borders of most studies that focus on students’ problem-solving behaviour pattern analyses in European cultures and education systems and examine Arabic students’ CPS behaviour. Results show that students in the Arabic school system interpret CPS problems the same way. That is, we confirmed the two-dimensional model of CPS, indicating the processes of knowledge acquisition and knowledge application as separate dimensions during the problem-solving process. Analyzing log data, we have identified large differences in students’ test-taking behavior in terms of the effectiveness of their exploration strategy, time-on-task, and number of trials at an international level (Jordan and Hungary). We identified four latent classes in both samples based on the students’ exploration strategy behavior. The tested process indicators proved to be non-invariant over the different latent profiles; that is, there are big differences in the role of the number of manipulations executed, time-on-task, and type of strategy used in actual problem-solving achievement between students that fall within different thinking profiles. Based on the results from the studies, we can conclude that online computer-based assessment is valid and reliable in the Jordanian higher education context. This study contributes to understanding how students from different educational contexts behave while solving complex problems