This study evaluated DEPTHS, an intelligent tutoring system for teaching software design patterns. The study assessed the system's effectiveness, the accuracy of the student model, and students' subjective experiences. Software engineering students from the University of Belgrade participated in the study. Their learning was evaluated using Kirkpatrick models, including reactions via questionnaire, learning through pre- and post-tests, behavior via student and peer feedback, and results showing students found DEPTHS helped them learn about design patterns and provided useful information, feedback, and advice.
This study used multilevel analysis to determine the predictive value of selected intrinsic factors (gender,
computer ownership, mathematics background and computer experience) and institutional type (an
extrinsic factor) on undergraduates’ Self Efficacy in Java Computer Programming (SEiJCP) in South –
West, Nigeria. The study adopted a correlational design. Purposive Sampling was used to select 254
computer science undergraduates from four universities (three federal-owned and one state-owned) in
south-west, Nigeria. Three research questions were answered. Two research instruments namely,
Computer experience scale (r = 0.84) and Java Programming Self Efficacy Scale (JPSES, r = 0.96) were
used to collect data. Data were analysed using descriptive statistics, and null and linear growth model
(LGM) procedures. The intercorrelation coefficients among the extrinsic factor, intrinsic factors and
SEiJCP were moderate. Null model shows that the variations in SEiJCP accounted for by insitutional level
differences was 99.0%. The fixed part of the LGM of intrinsic factors showed that only mathematical
backgroung contributed significantly (p < 0.05) to the prediction of SEiJCP. The random part of the LGM
showed no significant contributions of the interactions of the intrinsic factors, to the prediction of SEiJCP.
About 60.0% of the student level variation in SEiJCP is explained by the differences in intrinsic factors.
The institution – level variable had large predictive value on programming self efficacy. Computer science
departments should increase the number of mathematics courses in their curriculum.
Using Ontology in Electronic Evaluation for Personalization of eLearning Systemsinfopapers
I. Pah, F. Stoica, L. F. Cacovean, E. M. Popa, Using Ontology in Electronic Evaluation for Personalization of eLearning Systems, Proceedings of the 8th WSEAS International Conference on APPLIED INFORMATICS and COMMUNICATIONS (AIC’08), Rhodes, Greece, August 20-22, ISSN: 1790-5109, ISBN: 978-960-6766-94-7, pp. 332-337, 2008
Summary of two evaluation studies in educational technologyAmina Al Makhmari
This is a Summary of evaluation two studies in educational technology. The first summary focuses on the evaluation methodology used, in terms of purpose and instruments. The second focuses on the evaluation of specific technology features.
This study used multilevel analysis to determine the predictive value of selected intrinsic factors (gender,
computer ownership, mathematics background and computer experience) and institutional type (an
extrinsic factor) on undergraduates’ Self Efficacy in Java Computer Programming (SEiJCP) in South –
West, Nigeria. The study adopted a correlational design. Purposive Sampling was used to select 254
computer science undergraduates from four universities (three federal-owned and one state-owned) in
south-west, Nigeria. Three research questions were answered. Two research instruments namely,
Computer experience scale (r = 0.84) and Java Programming Self Efficacy Scale (JPSES, r = 0.96) were
used to collect data. Data were analysed using descriptive statistics, and null and linear growth model
(LGM) procedures. The intercorrelation coefficients among the extrinsic factor, intrinsic factors and
SEiJCP were moderate. Null model shows that the variations in SEiJCP accounted for by insitutional level
differences was 99.0%. The fixed part of the LGM of intrinsic factors showed that only mathematical
backgroung contributed significantly (p < 0.05) to the prediction of SEiJCP. The random part of the LGM
showed no significant contributions of the interactions of the intrinsic factors, to the prediction of SEiJCP.
About 60.0% of the student level variation in SEiJCP is explained by the differences in intrinsic factors.
The institution – level variable had large predictive value on programming self efficacy. Computer science
departments should increase the number of mathematics courses in their curriculum.
Using Ontology in Electronic Evaluation for Personalization of eLearning Systemsinfopapers
I. Pah, F. Stoica, L. F. Cacovean, E. M. Popa, Using Ontology in Electronic Evaluation for Personalization of eLearning Systems, Proceedings of the 8th WSEAS International Conference on APPLIED INFORMATICS and COMMUNICATIONS (AIC’08), Rhodes, Greece, August 20-22, ISSN: 1790-5109, ISBN: 978-960-6766-94-7, pp. 332-337, 2008
Summary of two evaluation studies in educational technologyAmina Al Makhmari
This is a Summary of evaluation two studies in educational technology. The first summary focuses on the evaluation methodology used, in terms of purpose and instruments. The second focuses on the evaluation of specific technology features.
MOOC Dropout Prediction Using Machine Learning Techniques: Review and Researc...Fisnik Dalipi
MOOC represents an ultimate way to deliver educational content in higher education settings by providing high-quality educational material to the students throughout the world. Considering the differences between traditional learning paradigm and MOOCs, a new research agenda focusing on
predicting and explaining dropout of students and low completion rates in MOOCs has emerged. However, due to different problem specifications and evaluation metrics, performing a comparative analysis of state-of-the-art machine learning architectures is a challenging task. In this paper, we provide an overview of the MOOC student dropout prediction phenomenon where machine learning techniques have been utilized. Furthermore, we highlight some solutions being used to tackle with dropout problem, provide an analysis about the challenges of prediction models, and propose some valuable insights and recommendations that might lead to developing useful and effective machine learning solutions to solve the MOOC dropout problem.
Presentation of
eTest Solution: Software Integrating Large- and Small-scale Assessment
by Srdjan Verbic, Srdjan Božovic and Saša Velickovic
(Proceedings if the IADIS international conference e-learning 2012, Lisbon, Portugal, July 17 - 20, 2012, ed. Miguel Baptista Nunes and Maggie McPherson, ISBN: 978-972-8939-71-7, pages 441-444)
The Architecture of System for Predicting Student Performance based on the Da...Thada Jantakoon
The goals of this study are to develop the architecture of a system for predicting student performance based on data science approaches (SPPS-DSA Architecture) and evaluate the SPPS-DSA Architecture. The research process is divided into two stages: (1) context analysis and (2) development and assessment. The data is analyzed by means of standardized deviations statistically. The research findings suggested that the SPPS-DSA architecture, according to the research findings, consists of three key components: (i) data source, (ii) machine learning methods and attributes, and (iii) data science process. The SPPS-DSA architecture is rated as the highest appropriate overall. Predicting student performance helps educators and students improve their teaching and learning processes. Predicting student performance using various analytical methods is reviewed here. Most researchers used CGPA and internal assessment as data sets. In terms of prediction methods, classification is widely used in educational data science. Researchers most commonly used neural networks and decision trees to predict student performance under classification techniques.
Technology Enabled Learning to Improve Student Performance: A SurveyIIRindia
The use of recent technology creates more impact in the teaching and learning process nowadays. Improvement of students’ knowledge by using the various technologies like smart class room environment, internet, mobile phones, television programs, use of iPods and etc. are play a very important role. Most of the education institutions used classroom teaching using advanced technologies such as smart class environment, visualization by power point projector and etc. This research work focusses on such technologies used for the improvement of student’s performance using some of the Data Mining (DM) techniques particularly classification and clustering. Information repositories (Educational Data Bases, Data Warehouses) are the source place for collecting study materials and use them for their learning purposes is the number one source for preparation of examinations. Particularly, this research work analyzes about the use of clustering and classification algorithms to enable the student’s performances and their learning capabilities using these modern technologies. During the study period, the student’s family background and their economic status are also play a very important role in their daily activities. These things are not considered in this survey work. A comparative study is carried out in this work by comparing students performance based on their results. The comparison is carried out based on the results of some of the classification and clustering algorithms. Finally, it states that the best algorithm for the improvement of students performance using these algorithms.
Technology Enabled Learning to Improve Student Performance: A SurveyIIRindia
The use of recent technology creates more impact in the teaching and learning process nowadays. Improvement of students’ knowledge by using the various technologies like smart class room environment, internet, mobile phones, television programs, use of iPods and etc. are play a very important role. Most of the education institutions used classroom teaching using advanced technologies such as smart class environment, visualization by power point projector and etc. This research work focusses on such technologies used for the improvement of student’s performance using some of the Data Mining (DM) techniques particularly classification and clustering. Information repositories (Educational Data Bases, Data Warehouses) are the source place for collecting study materials and use them for their learning purposes is the number one source for preparation of examinations. Particularly, this research work analyzes about the use of clustering and classification algorithms to enable the student’s performances and their learning capabilities using these modern technologies. During the study period, the student’s family background and their economic status are also play a very important role in their daily activities. These things are not considered in this survey work. A comparative study is carried out in this work by comparing students performance based on their results. The comparison is carried out based on the results of some of the classification and clustering algorithms. Finally, itstates that the best algorithm for the improvement of students performance using these algorithms.
Clustering Students of Computer in Terms of Level of ProgrammingEditor IJCATR
Educational data mining (EDM) is one of the applications of data mining. In educational data mining, there are two key domains, i.e. student domain and faculty domain. Different type of research work has been done in both domains.
In existing system the faculty performance has calculated on the basis of two parameters i.e. Student feedback and the result of student in that subject. In existing system we define two approaches one is multiple classifier approach and the other is a single classifier approach and comparing them, for relative evaluation of faculty performance using data mining
Techniques. In multiple classifier approach K-nearest neighbor (KNN) is used in first step and Rule based classification is used in the second step of classification while in single classifier approach only KNN is used in both steps of classification.
But in proposed system, I will analyse the faculty performance using 4 parameters i.e., student complaint about faculty, Student review feedback for faculty, students feedback, and students result etc.
For this proposed system I will be going to use opinion mining technique for analyzing performance of faculty and calculating score of each faculty.
MOOC Dropout Prediction Using Machine Learning Techniques: Review and Researc...Fisnik Dalipi
MOOC represents an ultimate way to deliver educational content in higher education settings by providing high-quality educational material to the students throughout the world. Considering the differences between traditional learning paradigm and MOOCs, a new research agenda focusing on
predicting and explaining dropout of students and low completion rates in MOOCs has emerged. However, due to different problem specifications and evaluation metrics, performing a comparative analysis of state-of-the-art machine learning architectures is a challenging task. In this paper, we provide an overview of the MOOC student dropout prediction phenomenon where machine learning techniques have been utilized. Furthermore, we highlight some solutions being used to tackle with dropout problem, provide an analysis about the challenges of prediction models, and propose some valuable insights and recommendations that might lead to developing useful and effective machine learning solutions to solve the MOOC dropout problem.
Presentation of
eTest Solution: Software Integrating Large- and Small-scale Assessment
by Srdjan Verbic, Srdjan Božovic and Saša Velickovic
(Proceedings if the IADIS international conference e-learning 2012, Lisbon, Portugal, July 17 - 20, 2012, ed. Miguel Baptista Nunes and Maggie McPherson, ISBN: 978-972-8939-71-7, pages 441-444)
The Architecture of System for Predicting Student Performance based on the Da...Thada Jantakoon
The goals of this study are to develop the architecture of a system for predicting student performance based on data science approaches (SPPS-DSA Architecture) and evaluate the SPPS-DSA Architecture. The research process is divided into two stages: (1) context analysis and (2) development and assessment. The data is analyzed by means of standardized deviations statistically. The research findings suggested that the SPPS-DSA architecture, according to the research findings, consists of three key components: (i) data source, (ii) machine learning methods and attributes, and (iii) data science process. The SPPS-DSA architecture is rated as the highest appropriate overall. Predicting student performance helps educators and students improve their teaching and learning processes. Predicting student performance using various analytical methods is reviewed here. Most researchers used CGPA and internal assessment as data sets. In terms of prediction methods, classification is widely used in educational data science. Researchers most commonly used neural networks and decision trees to predict student performance under classification techniques.
Technology Enabled Learning to Improve Student Performance: A SurveyIIRindia
The use of recent technology creates more impact in the teaching and learning process nowadays. Improvement of students’ knowledge by using the various technologies like smart class room environment, internet, mobile phones, television programs, use of iPods and etc. are play a very important role. Most of the education institutions used classroom teaching using advanced technologies such as smart class environment, visualization by power point projector and etc. This research work focusses on such technologies used for the improvement of student’s performance using some of the Data Mining (DM) techniques particularly classification and clustering. Information repositories (Educational Data Bases, Data Warehouses) are the source place for collecting study materials and use them for their learning purposes is the number one source for preparation of examinations. Particularly, this research work analyzes about the use of clustering and classification algorithms to enable the student’s performances and their learning capabilities using these modern technologies. During the study period, the student’s family background and their economic status are also play a very important role in their daily activities. These things are not considered in this survey work. A comparative study is carried out in this work by comparing students performance based on their results. The comparison is carried out based on the results of some of the classification and clustering algorithms. Finally, it states that the best algorithm for the improvement of students performance using these algorithms.
Technology Enabled Learning to Improve Student Performance: A SurveyIIRindia
The use of recent technology creates more impact in the teaching and learning process nowadays. Improvement of students’ knowledge by using the various technologies like smart class room environment, internet, mobile phones, television programs, use of iPods and etc. are play a very important role. Most of the education institutions used classroom teaching using advanced technologies such as smart class environment, visualization by power point projector and etc. This research work focusses on such technologies used for the improvement of student’s performance using some of the Data Mining (DM) techniques particularly classification and clustering. Information repositories (Educational Data Bases, Data Warehouses) are the source place for collecting study materials and use them for their learning purposes is the number one source for preparation of examinations. Particularly, this research work analyzes about the use of clustering and classification algorithms to enable the student’s performances and their learning capabilities using these modern technologies. During the study period, the student’s family background and their economic status are also play a very important role in their daily activities. These things are not considered in this survey work. A comparative study is carried out in this work by comparing students performance based on their results. The comparison is carried out based on the results of some of the classification and clustering algorithms. Finally, itstates that the best algorithm for the improvement of students performance using these algorithms.
Clustering Students of Computer in Terms of Level of ProgrammingEditor IJCATR
Educational data mining (EDM) is one of the applications of data mining. In educational data mining, there are two key domains, i.e. student domain and faculty domain. Different type of research work has been done in both domains.
In existing system the faculty performance has calculated on the basis of two parameters i.e. Student feedback and the result of student in that subject. In existing system we define two approaches one is multiple classifier approach and the other is a single classifier approach and comparing them, for relative evaluation of faculty performance using data mining
Techniques. In multiple classifier approach K-nearest neighbor (KNN) is used in first step and Rule based classification is used in the second step of classification while in single classifier approach only KNN is used in both steps of classification.
But in proposed system, I will analyse the faculty performance using 4 parameters i.e., student complaint about faculty, Student review feedback for faculty, students feedback, and students result etc.
For this proposed system I will be going to use opinion mining technique for analyzing performance of faculty and calculating score of each faculty.
A Survey on Research work in Educational Data Miningiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
The aim of our study is to extract the profiles of students activities, performed during the training sessions of a course of logic networks, and to relate such activities with the students’ performance at intermediate verification tests. In this course, undergraduate students learn and practice the concepts of logic networks with Deeds Simulator.
The Deeds is a set of educational tools for digital electronics, which stands for "Digital Electronics Education and Design Suite". It is used in courses of Electronic Engineering at DITEN, UNIGE.
By applying learning analytics methods to the data captured from activity logs and questionnaires, we aim to understand the learning behavior of students.
This project was presented at Learning Analytics Data Sharing – LADS14 Workshop at EC-TEL.
Educational Data Mining is used to find interesting patterns from the data taken from
educational settings to improve teaching and learning. Assessing student’s ability and performance with
EDM methods in e-learning environment for math education in school level in India has not been
identified in our literature review. Our method is a novel approach in providing quality math education
with assessments indicating the knowledge level of a student in each lesson. This paper illustrates how
Learning Curve – an EDM visualization method is used to compare rural and urban students’ progress
in learning mathematics in an e-learning environment. The experiment is conducted in two different
schools in Tamil Nadu, India. After practicing the problems the students attended the test and their
interaction data are collected and analyzed their performance in different aspects: Knowledge
component level, time taken to solve a problem, error rate. This work studies the student actions for
identifying learning progress. The results show that the learning curve method is much helpful to the
teachers to visualize the students’ performance in granular level which is not possible manually. Also it
helps the students in knowing about their skill level when they complete each unit.
Individual Differences in Multimedia Learning: An Application in a Computer S...idescitation
This study looked at the effects that individual
differences in prior knowledge have on student understanding
in learning with multimedia in a computer science subject.
Students were identified as either low or high prior knowledge
from a series of questions asked in a survey conducted at the
Faculty of Computer and Mathematical Sciences at University
Technology MARA, Malaysia. The subject domain chosen for
this study is a topic taught to undergraduates in the field of
Computer Sciences, in the subject of Operating Systems, i.e.,
Memory Management Concepts. This study utilizes a
multimedia application which is shown to a total of 257
students. Early results from the recall and transfer tests
indicate that students’ individual differences play a vital role
in learning outcome. As expected, the low prior knowledge
group scored significantly well in the recall tests as compared
to the transfer test, and the high prior knowledge group
performed comparatively better in the transfer test. This
suggests that educational designers who see to foster learning
and understanding should adopt the incorporation of learners’
prior knowledge as a design principle.
A Study on Learning Factor Analysis – An Educational Data Mining Technique fo...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
A Survey on Educational Data Mining TechniquesIIRindia
Educational data mining (EDM) creates high impact in the field of academic domain. The methods used in this topic are playing a major advanced key role in increasing knowledge among students. EDM explores and gives ideas in understanding behavioral patterns of students to choose a correct path for choosing their carrier. This survey focuses on such category and it discusses on various techniques involved in making educational data mining for their knowledge improvement. Also, it discusses about different types of EDM tools and techniques in this article. Among the different tools and techniques, best categories are suggested for real world usage.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Han’s Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insider’s LMA Course, this piece examines the course’s effects via a variety of Tim Han LMA course reviews and Success Insider comments.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
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Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
1. Study Title
Evaluating an Intelligent Tutoring System for Design Patterns: the DEPTHS
Experience.
Author
Zoran Jeremić1, Jelena Jovanović1 and Dragan Gašević2
1FON – School of Business Administration, University of Belgrade // jeremycod@yahoo.com // jeljov@gmail.com
2School of Computing and Information Systems, Athabasca University, Canada // dgasevic@acm.org.
The purpose of the study
This paper describes the evaluation study of DEPTHS, an intelligent tutoring
system for learning software design patterns, and aimed at assessing the system’s
effectiveness and the accuracy of the applied student model. It also targeted the
evaluation of the students’ subjective experiences with the system.
The audience:
software engineering students,. School of Business Administration, University
of Belgrade.
This study used Kirkpatrick Models:
1- Evaluation of reactions: by use an open-ended questionnaire.
2- Evaluation of learning: Endres and Kleiner
(1990) state that pretests and posttests are necessary when evaluating the
amount of learning that has taken place.
3- Evaluation of behavior: Feedback from students, their supervisors, and
peers as well as some other techniques can be used for collecting
information at this level.
4- Evaluation of results:
2. Students who learned with DEPTHS found that the system helped them to
learn a lot about design patterns.
They were especially pleased with the system’s ability to provide them with
many useful information, feedbackmessages and advice for further work.
Students’ responses indicated the need for regular communication with
teachers and other students as the underpinning priorities for successful
completion of online learning.
