Mobile technology in mathematics classroom
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This presentation shows the details of the study undertaken during one our graduate courses to examine the effects of using a mobile app in a mathematics classroom
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New technologies remain competitive in driving efforts to make learning more efficient. Technology-based assessment in special education has made quite some advancement (Goldsmith & LeBlanc, 2004). First applications of computer technology assessment were for the scoring student's test forms. Currently, features incorporate self-administration, software control in presentation, response evaluation based on algorithms, prescription based on expert knowledge and direct links in assessment and change in instructions. The technology-based assessment uses electronic and software systems to evaluate individual children in an educational setting. Traditional assessments employ approaches of the computer.
Video-based computer assisted test enabled learning of language for the student automatically increasing the validity of measurements. Video segments incorporated movie elements of moral dilemma in problem-solving tests. Students viewing the video segments respond by simply touching the screen. Innovative approaches have created relevance in testing procedures. Misplaced students result into poor results and get prompted to drop out. Teachers not well trained contribute to the misplacement due to poor management of certain behaviors and learning differences. For effect, teachers must be able to analyze data produced by the assessment and develop a due course of action.
In addressing students with physical limitations use of voice recognition, handwriting interpreters, stylus tools, and touchscreen enables communication without the use of keys (Gierach, 2009). New software features allow students to perform comfortable pace of video segments on preferred language options. Computers are linked to videodisc enabling students to learn according to individual needs and skills. Latest technological features concern evaluation. Technological advancements assess social competence among students. The evaluator views students in a variety of context. Limitation in technology infrastructure, seen as the key barrier in this sort of assessment. Many district schools lack adequate high-speed broadband access necessary for this evaluation. Moreover, obsolesce in technology-based assessment erodes the capacity to provide quality services technology-based systems have a relatively short functional life.
Holistic assessments are the best in technology-based assessments. They incorporate software control in presentation, conceptual models or algorithms, decision-making based rules and expert knowledge (Redecker, & Johannessen, 2013). Proliferation technology helps students in the inclusion of speech recognition, electronic communication, personal computers, robotics and artificial intelligence. Trends in technology-based assessments have impacted lives of students with a disability. They achieve school improvement goals as well as tracking student growth and progress. Current assessment norms have embedded current stan ...
ADABOOST ENSEMBLE WITH SIMPLE GENETIC ALGORITHM FOR STUDENT PREDICTION MODEL
Predicting the student performance is a great concern to the higher education managements.This
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enhance the quality of the higher educational system. Recently, a new direction is used for the improvement
of the classification accuracy by combining classifiers.In thispaper, we design and evaluate a fastlearning
algorithm using AdaBoost ensemble with a simple genetic algorithmcalled “Ada-GA” where the genetic
algorithm is demonstrated to successfully improve the accuracy of the combined classifier performance.
The Ada-GA algorithm proved to be of considerable usefulness in identifying the students at risk early,
especially in very large classes. This early prediction allows the instructor to provide appropriate advising
to those students. The Ada/GA algorithm is implemented and tested on ASSISTments dataset, the results
showed that this algorithm hassuccessfully improved the detection accuracy as well as it reduces the
complexity of computation.
A Software Measurement Using Artificial Neural Network and Support Vector Mac...
Today, Software measurement are based on various techniques such that neural network, Genetic
algorithm, Fuzzy Logic etc. This study involves the efficiency of applying support vector machine using
Gaussian Radial Basis kernel function to software measurement problem to increase the performance and
accuracy. Support vector machines (SVM) are innovative approach to constructing learning machines that
Minimize generalization error. There is a close relationship between SVMs and the Radial Basis Function
(RBF) classifiers. Both have found numerous applications such as in optical character recognition, object
detection, face verification, text categorization, and so on. The result demonstrated that the accuracy and
generalization performance of SVM Gaussian Radial Basis kernel function is better than RBFN. We also
examine and summarize the several superior points of the SVM compared with RBFN.
Jurnal 2014 student attitudes towards and use of ict in course study, work
This document summarizes a study that examined student attitudes towards and use of information and communication technology (ICT) in the contexts of course study, work, and social/leisure activities. The study used the technology acceptance model (TAM) as a framework to understand how perceptions of usefulness and ease of use influence technology adoption. Surveys were administered to students in six different courses. Factor analysis revealed that usefulness and ease of use were key dimensions for ICT attitudes across all three contexts, but that ICT was perceived most positively in the work context, and technology use at work was an important driver for technology use in other areas.
TitleEffect of Physical Education Teachers Computer Literacy o.docx
Title:
Effect of Physical Education Teachers' Computer Literacy on Technology Use in Physical Education. By: Kretschmann, Rolf, Physical Educator, 00318981, 2015 Special Issue, Vol. 72
Database:
MasterFILE Premier
Effect of Physical Education Teachers' Computer Literacy on Technology Use in Physical Education
Contents
1. Method
2. Results
3. Table 1 Computer Literacy and Instructional Technology and Media Use in PE
4. Table 1 (cont.)
5. Discussion
6. Conclusions
7. References
Listen
Teachers' computer literacy has been identified as a factor that determines their technology use in class. The aim of this study was to investigate the relationship between physical education (PE) teachers' computer literacy and their technology use in PE. The study group consisted of 57 high school level in-service PE teachers. A survey was used to assess the PE teachers' computer literacy and instructional technology and media use in PE. Quantitative statistical procedures were performed to analyze the data. The majority of the PE teachers did not often use technology in PE. PE teachers' computer literacy had an effect on their technology use in PE. PE teachers' use of information and communication technologies (ICTs) such as laptops, Internet, and digital cameras showed statistically significant differences in their computer literacy levels (low, average, and high). The surveyed PE teachers tended to not usetechnology in PE. However, the higher their computer literacy level was, the more likely they were to include technology in PE.
TEACHER EDUCATION
Technology has become normal and even ubiquitous in everyday life (Horst, 2012). The tech-savvy so-called digital natives (Bennett, Maton, & Kervin, 2008; Prensky, 2001), also known as the Net generation, "naturally" include diverse technologies in their daily routines. The society-wide technology enhancement also includes educational settings such as school. For school-aged children and adolescents, this means they are accompanied by technology not only in their leisure time, but also in their everyday life at school (Nemcek, 2013).
Technology as an instructional method has conquered school classrooms in the meantime (Calvani, 2009). Technology uses in schools have certainly been increased over the past decade (Wastiau et al., 2013). Among the school subjects, physical education (PE) and physical education teacher education (PETE) have been infused with technology as well, at least within the academic discussion and debate (Kretschmann, 2010; Leight & Nichols, 2012; Mohnsen, 2012; National Association for Sport and Physical Education, 2009).
With regard to research findings in the field of technology and PE, the empirical evidence is limited and few empirical studies are available (Kretschmann, 2010). Especially, the PE teachers' perspective has not been in the center of empirical studies so far. The majority of the studies have been focused on PETE students' information and communication technology (ICT) compete ...
final
This document describes a Computer Aided Testing System (CATS) designed to provide insight into students' reasoning patterns. CATS administers online tests and tracks students' responses, including response times and notes made on questions. It aims to emulate paper test-taking strategies. Test questions are randomly selected from pools of various difficulty levels. Student and teacher reports link performance to patterns in students' reasoning to support reflection and improve instruction.
Analysis of Educational Robotics activities using a machine learning approach
These slides present the preliminary results through the utilisation of machine learning techniques for the analysis of Educational Robotics activities. An experimentation with 197 secondary school students from Italy was con-ducted, through updating Lego Mindstorms EV3 programming blocks in order to record log files containing the coding sequences designed by the students (within team work), during the resolution of a preliminary Robotics’ exercise. We utilised four machine learning techniques (logistic regression, support vec-tor machine, K-nearest neighbors and random forests) to predict the students’ performance, comparing a supervised approach (using twelve indicators ex-tracted from the log files as input for the algorithms) and a mixed approach (ap-plying a k-means algorithm to calculate the machine learning features). The re-sults have highlighted that SVM with the mixed approach outperformed the other techniques, and that three learning styles were predominantly emerged from the data mining analysis.
Acceptence Of TPC Scale - Validity And Reliability Study
This document describes a study that developed and validated a scale to measure secondary students' acceptance of tablet computers (TPCs).
The researchers developed a 17-item scale based on the Technology Acceptance Model, which measures perceived usefulness, perceived ease of use, attitude toward use, and behavioral intention. They administered the scale to 400 high school students and analyzed it using exploratory and confirmatory factor analysis.
The analysis validated a 4-factor structure for the scale corresponding to the four dimensions. Perceived usefulness had the highest eigenvalue and explained the most variance. The validated scale provides a reliable tool to measure secondary students' acceptance of TPCs, which is important for effective integration and use of the technology in education
ANALYSIS OF STUDENT ACADEMIC PERFORMANCE USING MACHINE LEARNING ALGORITHMS:– ...
Student academic performance is the great value of institutes, universities and colleges. All colleges majorly focus on the career development of students. The academic performance of students plays a vital role in the establishment of a bright career. On the basis of better academic performance, the placement of the students will be better and the same will be reflected in the form of better admission and future. Machine learning can be deployed for the prediction of student performance. Various algorithms are playing an important role in the prediction of the accuracy of various machine learning models. These articles discuss various algorithms that can be helpful to deploy for predicting student academic performance. The article discusses various methods, predictive features and the accuracy of machine learning algorithms. The primary factors used for predicting students performance are academic institution, sessional marks, semester progress, family occupation, methods and algorithms. The accuracy level of various machine learning algorithms is discussed in this article.
CORRELATION BASED FEATURE SELECTION (CFS) TECHNIQUE TO PREDICT STUDENT PERFRO...
Education data mining is an emerging stream which helps in mining academic data for solving various
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student in engineering college depends on many factors. In this paper we have tried to implement a
classification technique to assist students in predicting their success in admission in an engineering
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in entrance examinations and historical data of previous batch of students. Feature selection is a process
for removing irrelevant and redundant features which will help improve the predictive accuracy of
classifiers. In this paper first we have used feature selection attribute algorithms Chi-square.InfoGain, and
GainRatio to predict the relevant features. Then we have applied fast correlation base filter on given
features. Later classification is done using NBTree, MultilayerPerceptron, NaiveBayes and Instance based
–K- nearest neighbor. Results showed reduction in computational cost and time and increase in predictive
accuracy for the student model
CORRELATION BASED FEATURE SELECTION (CFS) TECHNIQUE TO PREDICT STUDENT PERFRO...
This document discusses using feature selection and classification techniques to predict student performance and recommend an engineering stream for students. It first describes feature selection algorithms like chi-square and correlation-based feature selection to identify relevant attributes from a student data set. It then applies classifiers like NBTree, Naive Bayes, k-nearest neighbor, and multilayer perceptron on the selected features and evaluates their performance. The results show that correlation-based feature selection reduces computation time and improves predictive accuracy for recommending an engineering stream for students.
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The ImREAL project (http://www.imreal-project.eu) is researching how to meaningfully augment and extend existing experiential training simulators. The services developed support self-regulated, goal-, and application-oriented learning in adult training. We present results from a study evaluating a medical interview training simulator that has been augmented by an affective metacognitive scaffolding service and by user modelling exploiting social digital traces. Data from 152 medical students participating in this user trial were compared to the results of a prior trial on an earlier technology version. Findings show that students perceived the learning simulator positively and that the enhanced simulator led to increased feelings of success, less frustration, higher technical flow, and more reflection on learning. Interestingly, this cohort of users proved reluctant to provide their open social IDs to enrich their user models.
An Artificial Intelligence-Based Distance Education System Artimat
The document describes an artificial intelligence-based distance education system called ARTIMAT. It was developed to improve students' mathematical problem solving skills. The system was tested with 4 teachers and 59 students. Survey results found that the system was generally successful in helping students learn problem solving concepts and processes, but some interface changes were needed for students to adapt quickly. The system uses artificial intelligence techniques like forward and backward chaining to analyze problems and break them into sub-problems to guide students through the solution process step-by-step.
LENGUAGE TESTING (II Bimestre Abril Agosto 2011)
Universidad Técnica Particular de Loja
Ciclo Académico Abril Agosto 2011
Carrera: Inglés
Docente: Mgs. Orlando Lizaldes E.
Ciclo: Sexto
Bimestre: Segundo
Exploratory testing STEW 2016
The methods of exploratory testing has gained significant attention in industry and research in the last years. However, as many “buzzword" technologies, the introduction and application of exploratory testing is not straightforward. Exploratory testing it is not only black or white - scripted or exploratory - but also all shades of grey in between. Within the EASE industrial excellence center, we have executed an industrial workshop on exploratory testing, that helps providing understanding of how to choose feasible levels of exploration in exploratory testing. We will present the concepts of levels of exploration in exploratory testing, the outcomes of the workshop, along with relevant empirical research findings on exploratory testing.
ManganoHoeeg - Virtual Supermarket Designer POSTER
This document describes a virtual supermarket simulator application created by Emil Rosenlund Høeg and Luca Mangano. The application was developed for Aalborg University's Integrated Food Studies program as a tool to study consumer behavior and decision making in a virtual environment. A usability test was conducted with 17 participants to evaluate the application based on learnability, effectiveness, efficiency, errors, and satisfaction. The results showed that participants felt limited by the application's current functionality but provided suggestions for improvements to enhance the experience. The project demonstrated potential for a virtual simulation tool but requires further refinement of the user interface, textures, and interaction techniques to achieve its goals.
ASEE Paper Using Online Endless Quizzes as Graded Homework.pdf
This document summarizes a study on the use of online endless quizzes (EOQs) as graded homework in an undergraduate Numerical Methods course. The study found that EOQs improved student performance on topics covered by the quizzes, while minimizing grading time. Students had unlimited attempts on algorithmic quizzes covering three topics. Analysis found moderate relationships between Calculus III grades and scores on topics covered by EOQs. Student learning styles and self-efficacy were measured but found to have little influence on performance. Overall, EOQs enhanced student learning and demonstrated competency in topics covered by the quizzes.
An Adaptive Evaluation System to Test Student Caliber using Item Response Theory
Computational creativity research has produced many computational systems that are
described as creative [1]. A comprehensive literature survey reveals that although such systems are
labelled as creative, there is a distinct lack of evaluation of the Creativity of creative systems [1].
Nowadays, a number of online testing websites exist but the drawback of these tests is that every
student who gives a particular test will always be given the same set of questions irrespective of their
caliber. Thus, a student with a very high Intelligence Quotient (IQ) may be forced to answer basic
level questions and in the same way weaker students may be asked very challenging questions which
they cannot response. This method of testing results into a wastage of time for the high IQ students
and can be quite frustrating for the weaker students. This would never benefit a teacher to understand
a particular student’s caliber for the subject under Consideration. Each learner has different learning
status and therefore different test items should be used in their evaluation. This paper proposes an
Adaptive Evaluation System developed based on an Item Response Theory and would be created for
mobile end user keeping in mind the flexibility of students to attempt the test from anywhere. This
application would not only dynamically customize questions for students based on the previous
question he/she has answered but also by adjusting the degree of difficulty for test questions
depending on student ability, a teacher can acquire a valid & reliable measurement of student’s
competency.
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- 1. By: Ariel Eller, Cameron Nunan, Murat Akarsu, and Uzma Abdul Sattar Shaikh Purdue University EDPS 533 Spring 2014
- 2. The purpose of the study is: To examine the efficacy of using a digital technologies app (Wolfram Alpha) as a pedagogical tool for teaching students to plot quadratic equations. An experimental design used. Control group (no tech) and Experimental group (tech) Traditional classroom approach and Wolfram Alpha app.
- 3. Advantages use of technology: Technology has had a powerful role in educational advantages such as ubiquity, portability, and flexibility for collaborative learning projects (Park, 2008). Technology can enhance educational quality, make learning and teaching more engaging, and provide access to real life applications of the subject (Attard & Curry, 2012; Souter, 2001; Henderson & Yeow, 2012). Mobile devices as powerful tools
- 4. Disadvantages use of technology: Lack of experience Technical problems Cost of technological tools is expensive (Meche, Ross, and Vincent, 2002; Merrill, 2001; Henderson & Yeow, 2012; Franklin & Peng, 2008 ).
- 5. Wolfram Alpha can be accessed independently on smart phones, tablets or computers (Dimiceli, Lang, Locke, 2010). Easy to calculate mathematical equation as a computation engine Easy to draw parabolic graphs.
- 6. A set of power point slides which contained: Real time applications of quadratic equations Stepwise Derivation of the Quadratic Formula Sample Solution for a given quadratic equation Sample Graph for a given quadratic equation
- 7. Practice Test: Four questions to identify the correct graph (out of four options) for a given quadratic equation Three questions to solve for the roots of quadratic equations Posttest: Three questions to solve a quadratic equation Two questions to plot a quadratic equation on a graph One worded problem to check if they could translate their knowledge of quadratic equation to solve a real time problem
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- 10. Small Sample Size. Hence, findings cannot be generalized to a broader community. Participants had less amount of time to familiarize with the app. Two back to back Math related studies may have induced a mental fatigue among participants.