Search

In this study, the authors have developed a web-based programming exercise system currently implemented in classrooms. This system not only provides students with a web-based programming environment but also tracks the time spent on exercises, logging operations such as program editing, building, execution, and testing. Additionally, it records their results. For educators, the system offers insights into each student's progress, the evolution of their source code, and the instances of errors. While teachers find these functions beneficial, the method of providing feedback to students needs improvement. Immediate feedback is proven to be more effective for student learning. If the final course score could be predicted based on early data (e.g., from the 1st or 2nd week), students could adapt their study strategies accordingly. This paper demonstrates that one can predict the final score using the system's operational logs from the initial phases of the course. Furthermore, the score predictions can be revised weekly based on new class logs. We also explore the potential of offering tailored advice to students to enhance their final score. [For the full proceedings, see ED636095.]

These proceedings contain the papers and posters of the 5th International Conference on Educational Technologies 2017 (ICEduTech 2017), which has been organised by the International Association for Development of the Information Society and co-organised by the Western Sydney University, held in Sydney, Australia, 11-13 December 2017. ICEduTech is the scientific conference addressing the real topics as seen by teachers, students, parents and school leaders. Scientists, professionals and institutional leaders are invited to be informed by experts, sharpen the understanding what education needs and how to achieve it. Full papers presented in these proceedings include: (1) Bibliometric Science Mapping as a Popular Trend: Chosen Examples of Visualisation of International Research Network Results (Eugenia Smyrnova-Trybulska, Nataliia Morze, Olena Kuzminska and Piet Kommers); (2) Optimize Knowledge Sharing, Team Effectiveness, and Individual Learning within the Flipped Team-Based Classroom (Chung-Kai Huang, Chun-Yu Lin, Zih-Cin Lin, Cui Wang and Chia-Jung Lin); (3) Design and Development of an Interactive Multimedia Simulation for Augmenting the Teaching and Learning of Programming Concepts (Leonah L. Baloyi, Sunday O. Ojo and Etienne A. Van Wyk); (4) Introducing Tablets in a Portuguese School: A Micool Project Case Study Analysis (Miriam Judge); (5) College Communicative Teaching and e-Learning: A Training Scheme (Charito G. Ong; (6) Tested Strategies for Recruiting and Retention of STEM Majors (Sadegh Davari, Sharon Perkins-Hall and Krishani Abeysekera); (7) Redesigning Learning Spaces: What do Teachers Want for Future Classrooms? (Neuza Pedro); (8) Peer Instructions and Use of Technological Tools. An Innovative Methodology for the Development of Meaningful Learning (Oriel A. Herrera and Patricia Mejías); (9) It Doesn't Matter What is in Their Hands: Understanding How Students Use Technology to Support, Enhance and Expand Their Learning in a Complex World (Peter Bryant); (10) A Comparative Study on Social Media Addiction of High School and University Students (Ali Simsek, Kemal Elciyar and Taner Kizilhan); (11) Computer Literacy Teaching Using Peer Learning and under the Confucian Heritage Cultural Settings of Macao, China (Kelvin Wong, Ana Neves and Joao Negreiros); (12) Applying Sensors to Investigate Gender Differences in Beginning Tennis Players (Chih-Hung Yu, Jye-Shyan Wang and Cheng-Chih Wu); (13) Using Arduino to Teach Programming to First-Year Computer Science Students (Wee Lum Tan, Sven Venema and Ruben Gonzalez); (14) Dimensions of Self-Perceived Employability in First Year IT Students (Amy Antonio and David Tuffley); (15) Challenges for a New Generation of STEM Students (Krishani Abeysekera, Sharon Perkins-Hall, Sadegh Davari and Amanda Smith Hackler); (16) Developing a Gesture-Based Game for Mentally Disabled People to Teach Basic Life Skills (Mohammad Javad Nazirzadeh, Kürsat Cagiltay and Necdet Karasu); (17) Learning Group Formation for Massive Open Online Courses (MOOCs) (Sankalp Prabhakar and Osmar R. Zaiane) and (18) ICE: An Automated Tool for Teaching Advanced C Programming (Ruben Gonzalez). Short papers presented include: (1) MOOC as a Laboratory of Culture Shock: Helping Non-U.S. Students Integrate into All-American Virtual Environment (Valeri Chukhlomin and Anant Deshpande); (2) The Use of Mobile Devices Outside of the Classroom for Self-Directed Learning among Female EFL Students in Saudi Arabia (Fatimah Albedah and Chwee Beng Lee); (3) Communication Scaffolds for Project Management in PBL (Shigeru Sasaki, Masayuki Arai, Kumiko Takai, Mitsuhiro Ogawa and Hiroyoshi Watanabe); (4) Digital Pedagogies for Teachers' CPD (Matthew Montebello); (5) Sensing Locally in the Global Environment: Using Sensors in Teachers' Education (Maria João Silva, António Almeida, Bianor Valente, Margarida Rodrigues and Vítor Manteigas); (6) The Use of a Digital Badge as an Indicator and a Motivator (Jun Iwata, John Telloyan, Lynne Murphy, Shudong Wang and John Clayton; (7) Exploring the Education Potential of Minecraft: The Case of 118 Elementary-School Students (Thierry Karsenti and Julien Bugmann); (8) Digital Story Creation: Its Impact towards Academic Performance (Charito G. Ong); and (9) Collaborative Peer Feedback (David A. Smith). Posters include: (1) A System for Class Reflection Using iPads for Real-Time Bookmarking of Feedbacks into Simultaneously Recorded Videos (Taira Nakajima); (2) Roles, Strategies, and Impact of MOOCs on Flipping Business Education (Chung-Kai Huang, Chun-Yu Lin, Zih-Cin Lin and Cui Wang); and (3) Gamifying Outdoor Social Inquiry Learning with Context-Aware Technology (Morris Siu-Yung Jong, Tom Chan, Vincent Tam and Ming-Tak Hue). Individual papers include references, and an Author Index is included.

In the past eleven years we have developed several papers for ASCUE about hybrid classes. The initial paper we shared our experience developing a hybrid course and followed with comparisons of student opinions about face-to-face, hybrid, and online classes and finally a paper reviewing components that we were using in hybrid and online classes. It has been eight years since we last addressed the topic, in that time, we have gone from using no online classes and limited use of hybrid courses to relying on their availability in order to deliver our BS program in Computer and Information Technology (CIT) to three sites in Purdue Polytechnic statewide locations. In this paper we would like to take a look at where we have come in the last eleven years. We also would like to compare the methods of instruction: face-to-face, hybrid, and online, is one method more successful than others, how do students feel about the methods of instruction? We know the students are unique and those different types of course delivery may appeal to different people. With that in mind, we will look into the students who prefer which environment and why. Finally, if you are teaching a hybrid or online class what can be done in the classroom to be successful. [For the full proceedings, see ED596991.]

The "doer effect" is the assertion that the amount of interactive practice activity a student engages in is much more predictive of learning than the amount of passive reading or watching video the same student engages in. Although the evidence for a doer effect is now substantial, the evidence for a causal doer effect is not as well developed. To address this, we mined data for evidence of a causal doer effect across multiple domains. We examined data from two online courses in Psychology, one in Biology, one in Statistics, and two in Information Science, applying causal discovery algorithms in Tetrad to each. Assuming that factors driving a student's choices regarding how to spend their time in an online course are temporally prior to their performance on quizzes and exams, we found evidence of a causal relationship in every domain we studied. We did not find evidence that a unique causal model held in every domain we studied, but when we estimated the size of the causal relationships in the models we found in each domain, we did find evidence in every case that doing has a much stronger quantitative effect on learning than either reading or watching video. This work may be the first EDM effort to explore the generalizability of a causal claim about learning across multiple datasets from a variety of courses and contexts of use. It makes vivid the role of causal data mining algorithms in educational research. The evidence presented furthers the case for doer effect causality, but also recommends a need for richer data with more student background and learning process variables to better isolate causal directionality without assumptions about temporal order and unmeasured confounds. [For the full proceedings, see ED593090.]

Effective mining of data from online submission systems offers the potential to improve educational outcomes by identifying student habits and behaviours and their relationship with levels of achievement. In particular, it may assist in identifying students at risk of performing poorly, allowing for early intervention. In this paper we investigate different methods of following the development of student behaviour throughout the semester using online submission system data, and different approaches to analysing this development. We demonstrate the application of these methods to data from a junior computer science course (N=494) and discuss their usefulness in understanding the common behavioural strategies of students in this course and how these develop over time. Finally, we draw links between behaviour in weekly coding tasks and student performance in the final exam and discuss whether these methods could be applicable midway through the semester. [For the full proceedings, see ED592609.]

The IADIS CELDA 2012 Conference intention was to address the main issues concerned with evolving learning processes and supporting pedagogies and applications in the digital age. There had been advances in both cognitive psychology and computing that have affected the educational arena. The convergence of these two disciplines is increasing at a fast pace and affecting academia and professional practice in many ways. Paradigms such as just-in-time learning, constructivism, student-centered learning and collaborative approaches have emerged and are being supported by technological advancements such as simulations, virtual reality and multi-agents systems. These developments have created both opportunities and areas of serious concerns. This conference aimed to cover both technological as well as pedagogical issues related to these developments. The IADIS CELDA 2012 Conference received 98 submissions from more than 24 countries. Out of the papers submitted, 29 were accepted as full papers. In addition to the presentation of full papers, short papers and reflection papers, the conference also includes a keynote presentation from internationally distinguished researchers. Individual papers contain figures, tables, and references.

Students in two biostatistics courses at the Cornell Medical College and in a course in applications of computer science given in Cornell's School of Industrial Engineering were given access to an interactive package of computer programs enabling them to perform statistical analysis without the burden of hand computation. After a general discussion of the possible educational impact of the package, a brief report is given of its use in the above mentioned courses at Cornell. (Author)

This study was an examination of the usefulness of a statistical regression approach to identify prospective Engineering and Information and Computer Science (ICS) applicants most likely to succeed at the University of California at Irvine (UCI). The specific purpose was to determine the extent to which preadmissions measures such as high school grade point average (GPA) and admissions test scores could be used to predict college GPA. Multiple regression was used to maximize the correlation between the criterion variable (GPA) and predictor variables (preadmissions measures). Results indicate that cumulative GPA and major GPA of the presented samples of UCI Engineering and ICS majors can be reliably predicted using a linear combination of two preadmissions measures: high school GPA and the College Board Mathematics Achievement Test. None of the other three predictor variables (Scholastic Aptitude Test-Mathematics, Scholastic Aptitude Test-Verbal, and the College Board English Composition Achievement Test) added significantly to the predictions. Some shrinkage in the size of multiple correlations is expected when the equations of this study are applied to subsequent samples. However, in general, colleges that use the SAT and the high school record to predict freshmen GPA generally find that the results are fairly stable from year to year. (PN)