Search

For the fortieth time, the Research and Theory Division of the Association for Educational Communications and Technology (AECT) is sponsoring the publication of these Proceedings. Papers published in this volume were presented at the annual AECT Convention in Jacksonville, Florida. The Proceedings of AECT's Convention are published in two volumes. Volume 1 contains 19 papers dealing primarily with research and development topics. Fourteen papers dealing with the practice of instructional technology including instruction and training issues are contained in Volume 2. Volume 1 contains the following 19 papers: (1) Gamification for Change: A New Approach to Investigate Students' Attitudes toward Educational Gamification in Online Learning Environments (Sumayah Abu-Dawood); (2) Facilitating Higher Levels of Thinking and Deeper Cognitive Processing of Course Text Using Reciprocal Teaching Strategies in Asynchronous Discussion Forums (Jenifer R. Marquis and Ginger S. Watson); (3) Online Learning Design and Implementation Models: A Model Validation Study Using Expert Instructional Designers (Ann Armstrong and Albert Gale); (4) Tracking the Design and Development of a Six Module miniMOOC for Quality Graduate Supervision (Hawazen Alharbi and Michele Jacobsen); (5) Diversity Training in Organization Settings: Effective and Ethical Approaches for Change Leaders (Ashley McArthur and Nancy B. Hastings); (6) Hey, Want to Play? "Kahooting" to Win the Learning Game (Papia Bawa); (7) An Examination of Prior Knowledge and Cueing Effects in an Animation (Ismahan Arslan-Ari); (8) Teacher Perceptions of the Adaptation of the New Computer Science (CS) Curriculum: An Evaluation of CS Curriculum Implementation (Suhkyung Shin, Jongpil Cheon, and Sungwon Shin); (9) Multimedia Video Resolution, Camera Angle, and the Impact on Instructor Credibility and Immediacy (Miguel Ramlatchan and Ginger S. Watson); (10) The Effects of Visible-Annotation Tool on the Learning Process and Learning Outcome in CSCL (Yoonhee Shin, Jaewon Jung, and Dongsik Kim); (11) Pre-Service ICT Teachers' Recommendations for School Internet Safety (Sanser Bulu, Melike Kavuk-Kalender, and Hafize Keser); (12) Turkish Schools' Readiness for Preventing Cyberbullying (Melike Kavuk-Kalender, Hafize Keser, and Sanser Bulu); (13) Examining Technology Integration Decision-Making Processes and Identifying Professional Development Needs of International Teachers (Medha Dalal, Leanna Archambault, and Catharyn Shelton); (14) Integrating Learning Analytics into Workforce Education to Develop Self-Assessment Competency (Lin Zhong); (15) Ensuring Academic Integrity in Online Courses: A Case Analysis in Three Testing Environments (Berhane Teclehaimanot, Sue Ann Hochberg, Diana Franz, Mingli Xiao, and Jiyu You); (16) Changing Student Performance and Perceptions through Productive Failure: Active Learning for Applied Chemistry in Pharmaceutics (Dan Cernusca and Sanku Mallik); (17) The Construction of Sentiment Lexicon in Educational Field Based on Word2vec (Xiang Feng and Longhui Qiu); (18) Blended Instruction by Using Simulation Method Teaching to Enhance Digital Literacy for Student Teachers in Thailand (Sumalee Chuachai); and (19) Social Network Use Preferences of Pre-Service ICT Teachers (Omer Faruk Islim and Nese Sevim Cirak). (Individual papers contain references.) [For Volume 2, see ED580817.]

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 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.]

Presentations from a colloquium on applications of research on natural languages to computer science address the following topics: (1) analysis of complex adverbs; (2) parser use in computerized text analysis; (3) French language utilities; (4) lexicographic mapping of official language notices; (5) phonographic codification of Spanish; (6) electronic dictionaries; (7) specialized linguistic programs; (8) text difficulty; (9) linguistic variation and formalization in Quebec French; (10) French text generation software; (11) consequences for parsing of heterogeneity and insertion in sentences; (12) language utilities; (13) interaction of orthographic and phonological representations in reading; (14) automatic phoneticization of French texts; (15) computerized content analysis; (16) description of natural languages with a view to computer applications; (17) software to aid in the conception of deontic knowledge bases; (18) software for computer-assisted text generation; (19) organization of segment lengths in syllabic rhyme; (20) a language utility for public administration; (21) French prosody; (22) interactive treatment of documents; (23) transcription of oral corpuses from a comparative perspective; (24) a linguistic work station; (25) universal applicative grammar; (26) and the speed of synthesized speech. Other papers are abstracted only. (MSE)

The 4th International Conference on Educational Data Mining (EDM 2011) brings together researchers from computer science, education, psychology, psychometrics, and statistics to analyze large datasets to answer educational research questions. The conference, held in Eindhoven, The Netherlands, July 6-9, 2011, follows the three previous editions (Pittsburgh 2010, Cordoba 2009 and Montreal 2008), and a series of workshops within the AAAI, AIED, EC-TEL, ICALT, ITS, and UM conferences. The increase of e-learning resources such as interactive learning environments, learning management systems, intelligent tutoring systems, and hypermedia systems, as well as the establishment of state databases of student test scores, has created large repositories of data that can be explored to understand how students learn. The EDM conference focuses on data mining techniques for using these data to address important educational questions. The broad collection of research disciplines ensures cross fertilization of ideas, with the central questions of educational research serving as a unifying focus. This publication presents the following papers: (1) Social Information Discovery (Barry Smyth); (2) On exploration and mining of data in educational practice (Erik-Jan van der Linden, Martijn Wijffelaars, Thomas Lammers); (3) EDM and the 4th Paradigm of Scientific Discovery--Reflections on the 2010 KDD Cup Competition (John Stamper); (4) Factorization Models for Forecasting Student Performance (Nguyen Thai-Nghe, Tomas Horvath and Lars Schmidt-Thieme); (5) Analyzing Participation of Students in Online Courses Using Social Network Analysis Techniques (Reihaneh Rabbany Khorasgani, Mansoureh Takaffoli and Osmar Zaiane); (6) A Machine Learning Approach for Automatic Student Model Discovery (Nan Li, Noboru Matsuda, William W. Cohen and Kenneth R. Koedinger); (7) Conditions for effectively deriving a Q-Matrix from data with Non-negative Matrix Factorization (Michel C. Desmarais); (8) Student Translations of Natural Language into Logic: The Grade Grinder Translation Corpus Release 1.0 (Dave Barker-Plummer, Richard Cox and Robert Dale); (9) Instructional Factors Analysis: A Cognitive Model For Multiple Instructional Interventions (Min Chi, Kenneth Koedinger, Geoff Gordon, Pamela Jordan and Kurt Vanlehn); (10) The Simple Location Heuristic is Better at Predicting Students Changes in Error Rate Over Time Compared to the Simple Temporal Heuristic (A.F. Nwaigwe and K.R. Koedinger); (11) Items, skills, and transfer models: which really matters for student modeling? (Y. Gong and J.E. Beck); (12) Avoiding Problem Selection Thrashing with Conjunctive Knowledge Tracing (K.R. Koedinger, P.I. Pavlik Jr., J. Stamper, T. Nixon and S. Ritter); (13) Less is More: Improving the Speed and Prediction Power of Knowledge Tracing by Using Less Data (Bahador Nooraei, Zachary Pardos, Neil T. Heffernan and Ryan S.J.D. Baker); (14) Analysing frequent sequential patterns of collaborative learning activity around an interactive tabletop (R. Martinez Maldonado, K. Yacef, Judy Kay, A. Kharrufa and A. Al-Qaraghuli); (15) Acquiring Item Difficulty Estimates: a Collaborative Effort of Data and Judgment (K. Wauters, P. Desmet and W. Van Den Noortgate); (16) Spectral Clustering in Educational Data Mining (Shubhendu Trivedi, Zachary A. Pardos, Gabor Sarkozy and Neil T. Heffernan); (17) Does Time Matter? Modeling the Effect of Time with Bayesian Knowledge Tracing (Yumeng Qiu, Yingmei Qi, Hanyuan Lu, Zachary Pardos and Neil Heffernan); (18) Learning classifiers from a relational database of tutor logs (Jack Mostow, Jose Gonzalez-Brenes and Bao Hong Tan); (19) A Framework for Capturing Distinguishing User Interaction Behaviors in Novel Interfaces (S. Kardan and C. Conati); (20) How to Classify Tutorial Dialogue? Comparing Feature Vectors vs. Sequences (Jose Gonzalez-Brenes, Jack Mostow and Weisi Duan); (21) Automatically Detecting a Students Preparation for Future Learning: Help Use is Key (Ryan S.J.D. Baker, Sujith M. Gowda and Albert T. Corbett); (22) Ensembling Predictions of Student Post-Test Scores for an Intelligent Tutoring System (Zachary A. Pardos, Sujith M. Gowda, Ryan S.J.D. Baker and Neil T. Heffernan); (23) Improving Models of Slipping, Guessing, and Moment-By-Moment Learning with Estimates of Skill Difficulty (Sujith M. Gowda, Jonathan P. Rowe, Ryan S.J.D. Baker, Min Chi and Kenneth R. Koedinger); (24) A Method for Finding Prerequisites Within a Curriculum (Annalies Vuong, Tristan Nixon and Brendon Towle); (25) Estimating Prerequisite Structure From Noisy Data (Emma Brunskill); (26) What can closed sets of students and their marks say? (Dmitry Ignatov, Serafima Mamedova, Nikita Romashkin, and Ivan Shamshurin); (27) How university entrants are choosing their department? Mining of university admission process with FCA taxonomies (Nikita Romashkin, Dmitry Ignatov and Elena Kolotova); (28) What's an Expert? Using learning analytics to identify emergent markers of expertise through automated speech, sentiment and sketch analysis (Marcelo Worsley and Paulo Blikstein); (29) Using Logistic Regression to Trace Multiple Subskills in a Dynamic Bayes Net (Yanbo Xu and Jack Mostow); (30) Monitoring Learners Proficiency: Weight Adaptation in the Elo Rating System (K. Wauters, P. Desmet and W. Van Den Noortgate); (31) Modeling students activity in online discussion forums: a strategy based on time series and agglomerative hierarchical clustering (G. Cobo, D. Garcia, E. Santamaria, J.A. Moran, J. Melenchon and C. Monzo); (32) Prediction of Perceived Disorientation in Online Learning Environment with Random Forest Regression (Gokhan Akcapinar, Erdal Cosgun and Arif Altun); (33) Analysing Student Spatial Deployment in a Computer Laboratory (Vladimir Ivancevic, Milan Celikovic and Ivan Lukovic); (34) Predicting School Failure Using Data Mining (C. Marquez-Vera, C. Romero and S. Ventura); (35) A Dynamical System Model of Microgenetic Changes in Performance, Efficacy, Strategy Use and Value during Vocabulary Learning (P. Pavlik Jr. and S. Wu); (36) Desperately Seeking Subscripts: Towards Automated Model Parameterization (J. Mostow, Y. Xu and M. Munna); (37) Automatic Generation of Proof Problems in Deductive Logic (B. Mostafavi, T. Barnes and M. Croy); (38) Comparison of Traditional Assessment with Dynamic Testing in a Tutoring System (Mingyu Feng, Neil T. Heffernan, Zachary A. Pardos and Cristina Heffernan); (39) Evaluating a Bayesian Student Model of Decimal Misconceptions (G. Goguadze, S. Sosnovsky, S. Isotani and B. Mclaren); (40) Exploring user data from a game-like math tutor: a case study in causal modeling (D. Rai and J. E. Beck); (41) Goal Orientation and Changes of Carelessness over Consecutive Trials in Science Inquiry (A. Hershkovitz, R.S.J.D. Baker, J. Gobert and M. Wixon); (42) Towards improvements on domain-independent measurements for collaborative assessment (Antonio R. Anaya and Jesus G. Boticario); (43) A Java desktop tool for mining Moodle data (R. Pedraza-Perez, C. Romero and S. Ventura); (44) Using data mining in a recommender system to search for learning objects in repositories (A. Zapata-Gonzalez, V.H. Menendez, M.E. Prieto-Mendez and C. Romero); (45) E-learning Web Miner: A data mining application to help instructors involved in virtual courses (Diego Garcia-Saiz and M.E. Zorrilla Pantaleon); (46) Computerized Coding System for Life Narratives to Assess Students' Personality Adaption (Q. He, B.P. Veldkamp and G.J. Westerhof); (47) Partially Observable Sequential Decision Making for Problem Selection in an Intelligent Tutoring System (Emma Brunskill and Stuart Russell); (48) Mining Teaching Behaviors from Pedagogical Surveys (J. Barracosa and C. Antunes); (49) Variable Construction and Causal Modeling of Online Education Messaging Data: Initial Results (S. Fancsali); (50) The Hospital Classrooms Environments Challenge (Carina Gonzalez and Pedro A. Toledo); (51) Combining study of complex network and text mining analysis to understand growth mechanism of communities on SNS (Osamu Yamakawa, Takahiro Tagawa, Hitoshi Inoue, Koichi Yastake and Takahiro Sumiya); (52) Logistic Regression in a Dynamic Bayes Net Models Multiple Subskills Better! (Yanbo Xu and Jack Mostow); (53) Studying problem-solving strategies in the early stages of learning programming (E. Cambranes-Martinez and J. Good); (54) Brick: Mining Pedagogically Interesting Sequential Patterns (Anjo Anjewierden, Hannie Gijlers, Nadira Saab and Robert De Hoog); (55) Intelligent evaluation of social knowledge building using conceptual maps with MLN (L. Moreno, C.S. Gonzalez, R. Estevez and B. Popescu); (56) Identifying Influence Factors on Students Success by Subgroup Discovery (F. Lemmerich, M. Ifland and F. Puppe); (57) Analyzing University Data for Determining Student Profiles and Predicting Performance (D. Kabakchieva, K. Stefanova and V. Kisimov); (58) The EDM Vis Tool (Matthew Johnson, Michael Eagle, Leena Joseph and Tiffany Barnes); (59) Towards Modeling Forgetting and Relearning in ITS: Preliminary Analysis of ARRS Data (Y. Wang and N.T. Heffernan); (60) Quality Control and Data Mining Techniques Applied to Monitoring Scaled Scores (A.A. Von Davier); (61) eLAT: An Exploratory Learning Analytics Tool for Reflection and Iterative Improvement of Technology Enhanced Learning (A.L. Dyckhoff, D. Zielke, M.A. Chatti and U. Schroeder); (62) Predicting graduate-level performance from undergraduate achievements (J. Zimmermann, K.H. Brodersen, J.-P. Pellet, E. August and J.M. Buhmann); (63) Mining Assessment and Teaching Evaluation Data of Regular and Advanced Stream Students (Irena Koprinska); (64) Investigating Usage of Resources in LMS with Specific Association Rules (A. Merceron); (65) Towards Parameter-Free Data Mining: Mining Educational Data with "yacaree" (Jose L. Balcazar, Diego Garcia-Saiz and Marta E. Zorrilla); (66) Factors Impacting Novice Code Comprehension in a Tutor for Introductory Computer Science (Leigh Ann Sudol-DeLyser and Jonathan Steinhart); (67) Investigating the Transitions between Learning and Non-learning Activities as Students Learn Online (P.S. Inventado, R. Legaspi, M. Suarez and M. Numao); (68) Learning parameters for a knowledge diagnostic tools in orthopedic surgery (S. Lalle and V. Luengo); (69) Problem Response Theory and its Application for Tutoring (P. Jarusek and R. Pelanek); and (70) Towards Better Understanding of Transfer in Cognitive Models of Practice (Michael V. Yudelson, Philip I. Pavlik, Jr. and Kenneth R. Koedinger). Individual papers contain tables, figures, footnotes and references.

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.]

COVID-19 presented a challenge to the traditional methods of teaching programming and robotics in a secondary school environment. When campuses were closed around the world in the spring of 2020, it was not possible for students to access the computer labs nor the robotics equipment that was traditionally used to facilitate the instruction of robotics programming units. This paper presents a design research project in which two secondary institutions in Canada and Turkey collaborated to teach computer science and robotics programming, specifically reinforcement learning, through the use of an online simulation environment. The two student cohorts in the study both were successful in developing reinforcement learning models for autonomous vehicles, despite not having any prior experience in machine learning nor artificial intelligence. The implications of this work are that physical robotics kits and dedicated robotics spaces are not essential to the teaching of programming and robotics. This is especially relevant to marginalized communities that do not have the resources to support robotics instruction, further exacerbating the digital divide.

"Proceedings of International Conference on Research in Education and Science" includes full papers presented at the International Conference on Research in Education and Science (ICRES) which took place on March 24-27, 2022 in Antalya, Turkey. The aim of the conference is to offer opportunities to share ideas, to discuss theoretical and practical issues and to connect with the leaders in the fields of education and science. The conference is organized annually by the International Society for Technology, Education, and Science (ISTES). The ICRES invites submissions which address the theory, research or applications in all disciplines of education and science. The ICRES is organized for: faculty members in all disciplines of education and science, graduate students, K-12 administrators, teachers, principals and all interested in education and science. After peer-reviewing process, all full papers are published in the Conference Proceedings. [Individual papers are indexed in ERIC.]

This paper aims to propose a framework to improve the quality in teaching and learning in order to develop good practices to train professionals in the career of computer engineering science. To demonstrate the progress and achievements, our work is based on two principles for the formation of professionals, one based on the model of learning skills and the second on the model of convergence in information technology and communication (ICT) (Bradley, 2005). In this paper, the results are the validation of the graduation profile as part of the process of self-assessment and the internal strategies of the learning community that allowed to achieve the career validation for five years (2015-2020). [For full proceedings, see ED571459.]

At present, in the literature that is are devoted to social life, it has become commonplace to claim that we live in the era of the "digital revolution". Our paper deals with computer science in the period from the late 40's to early 60's of the twentieth century, which is considered from the point of view of "intellectual history". We concern ourselves with, in the main, two problems. First is self-consciousness of members of the computer science community in relation to other spheres of scientific and engineering-practical activity, including the question about the correlation of theoretical and applied components in the structure of the knowledge. Secondly, we tell about interactions of a new sphere of professional activity have been occurred in this period with various social practices - scientific, industrial, political and so on. Based on the texts that reflect the incipience of computer science in the West and in the Soviet Union, we reveal some features of the "philosophy" of the early period of the digital revolution in the context of the existence of two ideological systems. [For "NORDSCI International Conference Proceedings: Education and Language Edition (Helsinki, Finland, July 17, 2018). Book 1. Volume 1," see ED603189.]

The author has attended and presented at most ASCUE meetings since 1994, and has worked professionally in research and development, industry, military, government, business, and private and public academia--moving between computer science, software engineering, and business fields at both the undergraduate and graduate level, and even running academic computing for a few years. This paper/session will present/discuss definitions, implications, and relationships of and between the areas of computer science, software engineering, information technology, and business information systems. Included will be perspectives of the history of the past, specific needs of the present, and general directions and predictions of the future, and the implications to academia, business, and other areas. [For the full proceedings, see ED575713.]

Due to increasing digitization in all aspects of life, the demand for qualified software development professionals continues to increase. Students from underrepresented groups, such as first generation students from non-academic families, minorities, single parents and women represent an underutilized pool of untapped potential talent. The question arises as to which unique perspectives computer science graduates from underrepresented groups can bring to software development companies. In addition to programming skills, non-technical competencies, such as foreign language abilities, intercultural communication, creativity, conflict management, team-building and organizational skills are vital for success in diverse, international project teams. A large job market database for new graduates, developed for a consortium of universities in Bavaria, Germany, is analyzed using machine learning tools. Career competencies desired by recruiting companies are compared to potential advantages offered by computer science graduates from underrepresented groups. [For the complete proceedings, see ED608557.]

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.]

Making construals is a practical approach to computing that was originally developed for and by computer science undergraduates. It is the central theme of an EU project aimed at disseminating the relevant principles to a broader audience. This involves bringing together technical experts in making construals and international experts in educational technology in organising wide-ranging learning activities. This paper, which complements the tutorial and discussion on making construals in (Beynon et al. 2015a, 2015b), documents issues that arise from this encounter. Its central argument is that making construals offers a significant new contribution to educational technology. [For the full proceedings, see ED562093.]

A quality educational experience for secondary students involves more than an acquisition of content knowledge; it entails providing students opportunities to develop a variety of thinking skills that enable integration of knowledge and the promotion of student self-directed learning outside of the classroom. One critical skill that is often underemphasized in education is computational thinking. The purpose of this conceptual paper is to discuss the parallels between the processes of computational thinking and self-regulated learning, and the corresponding implications of this integrated framework for instruction in secondary classrooms. Guiding our analysis is the premise that because computational thinking processes can be viewed as goal-directed processes, it is possible to use self-regulated learning theory as a framework for assessing and enhancing computational thinking. Secondary educators have minimal experience with teaching computational thinking in the United States, so not only is a clear definition of computational thinking necessary in the Next Generation Science Standards, it is also necessary to have a learning theory from which to structure this type of thinking. [For the full proceedings, see ED562093.]

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.]

The theme of 'embodiment' has become an important approach in current mathematics education research, growing in significance from the mid-1990s onward. However terminology of 'body' and 'embodiment' is used to signal multiple, widely varying meanings in this research. Studies are grounded in a number of radically different theoretical bases, with the result that mathematics education researchers do not necessarily mean the same thing at all when they refer to 'body', 'embodiment' and 'embodied mathematics learning'. In this theoretical paper, the author offers a framework for interpreting these polysemous terms in relation to their theoretical groundings, with examples from the mathematics education literature. [For the complete proceedings, see ED597799.]

This paper surveys approaches that are deemed practical for instructional use of radios and computers by satellite transmission. For each of the two instructional technologies a brief history is provided, a survey of the evaluation studies of effectiveness is given, and a concluding section on planning for application is provided. Because the literature on these topics is extensive, an attempt is made to select for discussion items that are deemed of special importance or of particular use to field users. The paper concluded with some general recommendations for use of either instructional radio or computer assisted instruction by means of satellite transmission. A selected bibliography is appended to the paper. (Author)

This paper attempts to characterize some of the knowledge that people have of their language's semantic structure, in order to determine what a computer has to know to perform the operations of searching for facts and answering questions. Necessarily, this sort of semantic analysis is done in connection with syntactic structure. The meaningful elements of English are divided into sets of words and sets of sentences. Their general properties are examined as well as the general relations between elements. The paper concludes with a discussion of the controversy between generative semanticists and interpretivists over syntactic and semantic structure. The work of logicians on artificial language is cited as being helpful in explanations of syntactic and semantic structure in natural languages. (AM)

Using the principles of biology, along with engineering and with the help of computer, scientists manage to copy. DNA sequences from nature and use them to create new organisms. DNA is created through engineering and computer science managing to create life inside a laboratory. We cannot dismiss the role that synthetic biology could lead in solving the most pressing problems of humanity. We are using technology to control live organisms and use them as we wish. But how should that knowledge be accessed? And which way is the best way so everyone can benefit from this technology and its developments? Open sources software could be the answer. [For full proceedings, see ED571459.]

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.

The 4th international conference "Nation and Language: Modern Aspects of Socio-Linguistic Development" continues an eight-year old tradition. The conference is organized by Kaunas University of Technology Panevezys Institute and aims to bring scientists and researchers together for a general scientific discussion on new trends in sociolinguistic, language research, teaching and learning. The general goal of the conference is to present an overview of the most current approaches to language attitudes and motivation, to illuminate the sociolinguistic and educational significance of the topics. It is necessary to understand how teachers and scientists can carry out their charge while respecting the languages and language varieties that students bring to school or universities and using existing language skills to build new ones, how teachers can enable students to achieve the linguistic mastery that would allow them to have more than grammatical competence in order to be able to communicate effectively in a language. Current theory and research have provided clear indications that the relationships between a person's prior linguistic and academic experience, the social context of instruction, and the results of formal language instruction have complex and reciprocal connections with each other. Positive attitudes about language and language learning may be as much the result of the success as the cause. The conference pays special attention to different aspects of languages and cultures, language contact and change, language maintenance, shift and loss, language and social inequalities, language learning and teaching and language planning and policy. The conference covers wide range of topics like: (1) Theoretical and practical aspects of language education; (2) Good practice and innovations in higher education language teaching; (3) Socio-linguistic aspects in language teaching and learning; (4) Applying new technologies in language teaching; (5) Research of language peculiarities and their integration into the teaching process and lexicographical practice; and (6) Problems of terminology and translation; international communication. The following papers are included in the proceedings: (1) Why Standard Spoken Language of Students Gets Poorer? (R. Bartkeviciene and N. Petniuniene); (2) On Foreign Language Teaching and Learning Conditions in Moldovan Higher Education Institutions (M. Fuciji); (3) Identity and Languages - Results of a Local Research (T. Hrivikova); (4) Computer Linguistics and Pragmatics (G. Klimoviene and J. Korsakas); (5) Social and Cultural Contexts in Teaching Foreign Languages of Young People (J. Kida); (6) Responsibility Development in Practical Foreign Language Classes (I. Kildiene and R. Lazauskiene); (7) Terms of Computer Science in Professional Environment, Texts and Dictionaries (I. Klijunaite and K. Karklius); (8) Developing Student Responsibility in the Process of Foreign Language Acquisition (G. Klimoviene, R. Barzdziukiene, and N. Vaitkiene); (9) Foreign Words and Foreign Words Significance in Children's Language Development (V. Kuzina); (10) People and Language - Slovenia (J. Lipnik); (11) The Systemic Approach to Emotional Potential Development and Language Acquisition in the Context of European Linguistic Unity (T. Lobanova-Shunina and Y. Shunin); (12) Teaching of English as a Second Language in ICT-Based Learning Settings (V. Narbutas); (13) Grammatical Variables in the Speeches of Barack Obama and Michelle Obama (A. Nauseda and I. Klijunaite); (14) Understanding the Impact of Culture on Economic Development (I. Nemethova); (15) The Impact of English on the Slovak Language Culture (M. Pauleova); (16) Benefiting from a Text-Oriented EFL/ESP Course (G. Snuviskiene); (17) Shaping Modern Serbian Ethnolinguistic Nation and National State-Building in the First Half of the 19th C (V. B. Sotirovic); (18) Terminology: The Term Variance in the English and Lithuanian Special Language of Economics (V. Stankeviciene and D. Svencioniene); (19) The Grammatical Metaphor as a Sociolinguistic Marker in Horror Stories (S. Susinskiene and E. Jurkoniene); (20) Insights on Problems of Public Speaking and Ways of Overcoming It (D. Susniene, R. Virbickaite, and O. Purvinis); (21) Sociology and Society: Towards Diversity (A. Vosiliute); and (22) Disabled People: The Problems of Identity (A. Vosiliute). An author index is also included. (Individual papers contain tables, figures, references, and footnotes.) [Abstract has been modified to meet ERIC guidelines.]

The Third International Conference on Data Mining (EDM 2010) was held in Pittsburgh, PA, USA. It follows the second conference at the University of Cordoba, Spain, on July 1-3, 2009 and the first edition of the conference held in Montreal in 2008, and a series of workshops within the AAAI, AIED, EC-TEL, ICALT, ITS, and UM conferences. EDM 2011 will be held in Eindhoven, Netherlands. EDM brings together researchers from computer science, education, psychology, psychometrics, and statistics to analyze large data sets to answer educational research questions. The increase in instrumented educational software and databases of student test scores, has created large repositories of data reflecting how students learn. The EDM conference focuses on computational approaches for analyzing the data to address important educational questions. The broad collection of research disciplines ensures cross fertilization of ideas, with the central questions of educational research serving as a unifying focus. This publication presents the following papers: (1) Effort-based Tutoring: An Empirical Approach to Intelligent Tutoring (Ivon Arroyo, Hasmik Mehranian and Beverly P. Woolf); (2) An Analysis of the Differences in the Frequency of Students' Disengagement in Urban, Rural, and Suburban High Schools (Ryan S.J.d. Baker and Sujith M. Gowda); (3) On the Faithfulness of Simulated Student Performance Data (Michel C. Desmarais and Ildiko Pelczer); (4) Mining Bodily Patterns of Affective Experience during Learning (Sidney D'Mello and Art Graesser); (5) Can We Get Better Assessment From A Tutoring System Compared to Traditional Paper Testing? Can We Have Our Cake (Better Assessment) and Eat It too (Student Learning During the Test)? (Mingyu Feng and Neil Heffernan); (6) Using Neural Imaging and Cognitive Modeling to Infer Mental States while Using an Intelligent Tutoring System (Jon M. Fincham, John R. Anderson, Shawn Betts and Jennifer Ferris); (7) Using multiple Dirichlet distributions to improve parameter plausibility (Yue Gong, Joseph E. Beck and Neil T. Heffernan); (8) Examining Learner Control in a Structured Inquiry Cycle Using Process Mining (Larry Howard, Julie Johnson and Carin Neitzel); (9) Analysis of Productive Learning Behaviors in a Structured Inquiry Cycle Using Hidden Markov Models (Hogyeong Jeong, Gautam Biswas, Julie Johnson and Larry Howard); (10) Data Mining for Generating Hints in a Python Tutor (Anna Katrina Dominguez, Kalina Yacef and James R. Curran); (11) Off Topic Conversation in Expert Tutoring: Waste of Time or Learning Opportunity (Blair Lehman, Whitney Cade and Andrew Olney); (12) Sentiment Analysis in Student Experiences of Learning (Sunghwan Mac Kim and Rafael A. Calvo); (13) Online Curriculum Planning Behavior of Teachers (Keith E. Maull, Manuel Gerardo Saldivar and Tamara Sumner); (14) A Data Model to Ease Analysis and Mining of Educational Data (Andre Kruger, Agathe Merceron and Benjamin Wolf); (15) Identifying Students' Inquiry Planning Using Machine Learning (Orlando Montalvo, Ryan S.J.d. Baker, Michael A. Sao Pedro, Adam Nakama and Janice D. Gobert); (16) Skill Set Profile Clustering: The Empty K-Means Algorithm with Automatic Specification of Starting Cluster Centers (Rebecca Nugent, Nema Dean and Elizabeth Ayers); (17) Navigating the parameter space of Bayesian Knowledge Tracing models: Visualizations of the convergence of the Expectation Maximization algorithm (Zachary Pardos and Neil Heffernan); (18) Mining Rare Association Rules from e-Learning Data (Cristobal Romero, Jose Raul Romero, Jose Maria Luna and Sebastian Ventura); (19) Using Text Replay Tagging to Produce Detectors of Systematic Experimentation Behavior Patterns (Michael Sao Pedro, Ryan S.J.d. Baker, Orlando Montalvo, Adam Nakama and Janice D. Gobert); (20) Identifying High-Level Student Behavior Using Sequence-based Motif Discovery (David H. Shanabrook, David G. Cooper, Beverly Park Woolf and Ivon Arroyo); (21) Unsupervised Discovery of Student Strategies (Benjamin Shih, Kenneth R. Koedinger and Richard Scheines); (22) Assessing Reviewer's Performance Based on Mining Problem Localization in Peer-Review Data (Wenting Xiong, Diane Litman and Christian Schunn); (23) Using Numeric Optimization To Refine Semantic User Model Integration Of Adaptive Educational Systems (Michael Yudelson, Peter Brusilovsky, Antonija Mitrovic and Moffat Mathews); (24) An Annotations Approach to Peer Tutoring (John Champaign and Robin Cohen); (25) Using Educational Data Mining Methods to Study the Impact of Virtual Classroom in E-Learning (Mohammad Hassan Falakmasir and Jafar Habibi); (26) Mining Students' Interaction Data from a System that Support Learning by Reflection (Rajibussalim); (27) Process Mining to Support Students' Collaborative Writing (Vilaythong Southavilay, Kalina Yacef and Rafael A. Callvo); (28) Automatic Rating of User-Generated Math Solutions (Turadg Aleahmad, Vincent Aleven and Robert Kraut); (29) Tracking Students' Inquiry Paths through Student Transition Analysis (Matt Bachmann, Janice Gobert and Joseph Beck); (30) DISCUSS: Enabling Detailed Characterization of Tutorial Interactions Through Dialogue Annotation (Lee Becker, Wayne H. Ward and Sarel vanVuuren); (31) Data Mining of both Right and Wrong Answers from a Mathematics and a Science M/C Test given Collectively to 11,228 Students from India [1] in years 4, 6 and 8 (James Bernauer and Jay Powell); (32) Mining information from tutor data to improve pedagogical content knowledge (Suchismita Srinivas, Muntaquim Bagadia and Anupriya Gupta); (33) Clustering Student Learning Activity Data (Haiyun Bian); (34) Analyzing Learning Styles using Behavioral Indicators in Web based Learning Environments (Nabila Bousbia, Jean-Marc Labat, Amar Balla and Issam Rebai); (35) Using Topic Models to Bridge Coding Schemes of Differing Granularity (Whitney L. Cade and Andrew Olney); (36) A Distillation Approach to Refining Learning Objects (John Champaign and Robin Cohen); (37) A Preliminary Investigation of Hierarchical Hidden Markov Models for Tutorial Planning (Kristy Elizabeth Boyer, Robert Phillips, Eun Young Ha, Michael D. Wallis, Mladen A. Vouk, and James C. Lester); (38) Higher Contributions Correlate with Higher Learning Gains (Carol Forsyth, Heather Butler, Arthur C. Graesser, Diane Halpern); (39) Pinpointing Learning Moments; A finer grain P(J) model (Adam Goldstein, Ryan S.J.d. Baker and Neil T. Heffernan); (40) Predicting Task Completion from Rich but Scarce Data (Jose P. Gonzalez-Brenes and Jack Mostow); (41) Hierarchical Structures of Content Items in LMS (Sharon Hardof-Jaffe, Arnon Hershkovitz, Ronit Azran and Rafi Nachmias); (42) Is Students' Activity in LMS Persistent? (Arnon Hershkovitz and Rafi Nachmias); (43) EDM Visualization Tool: Watching Students Learn (Matthew M. Johnson and Tiffany Barnes); (44) Inferring the Differential Student Model in a Probabilistic Domain Using Abduction inference in Bayesian networks (Nabila Khodeir, Nayer Wanas, Nevin Darwish and Nadia Hegazy); (45) Using LiMS (the Learner Interaction Monitoring System) to Track Online Learner Engagement and Evaluate Course Design (Leah P. Macfadyen and Peter Sorenson); (46) Observing Online Curriculum Planning Behavior of Teachers (Keith E. Maull, Manuel Gerardo Saldivar and Tamara Sumner); (47) When Data Exploration and Data Mining meet while Analysing Usage Data of a Course (Andre Kruger, Agathe Merceron and Benjamin Wolf); (48) AutoJoin: Generalizing an Example into an EDM query (Jack Mostow and Bao Hong (Lucas) Tan); (49) Conceptualizing Procedural Knowledge Targeted at Students with Different Skill Levels (Martin Mozina, Matej Guid, Aleksander Sadikov, Vida Groznik, Jana Krivec, and Ivan Bratko); (50) Data Reduction Methods Applied to Understanding Complex Learning Hypotheses (Philip I. Pavlik Jr.); (51) Analysis of a causal modeling approach: a case study with an educational intervention (Dovan Rai and Joseph E. Beck); (52) Peer Production of Online Learning Resources: A Social Network Analysis (Beijie Xu and Mimi M. Recker); (53) Class Association Rules Mining from Students' Test Data (Cristobal Romero, Sebastian Ventura, Ekaterina Vasilyeva and Mykola Pechenizkiy); (54) Modeling Learning Trajectories with Epistemic Network Analysis: A Simulation-based Investigation of a Novel Analytic Method for Epistemic Games (Andre A. Rupp, Shauna J. Sweet and Younyoung Choi); (55) Multiple Test Forms Construction based on Bees Algorithm (Pokpong Songmuang and Maomi Ueno); (56) Can Order of Access to Learning Resources Predict Success? (Hema Soundranayagam and Kalina Yacef); (57) A Data Driven Approach to the Discovery of Better Cognitive Models (Kenneth R. Koedinger and John C. Stamper); (58) Using a Bayesian Knowledge Base for Hint Selection on Domain Specific Problems (John C. Stamper, Tiffany Barnes and Marvin Croy); (59) A Review of Student Churn in the Light of Theories on Business Relationships (Jaan Ubi and Innar Liiv); (60) Towards EDM Framework for Personalization of Information Services in RPM Systems (Ekaterina Vasilyeva, Mykola Pechenizkiy, Aleksandra Tesanovic, Evgeny Knutov, Sicco Verwer and Paul De Bra); (61) A Case Study: Data Mining Applied to Student Enrollment (Cesar Vialardi, Jorge Chue, Alfredo Barrientos, Daniel Victoria, Jhonny Estrella, Juan Pablo Peche and Alvaro Ortigosa); (62) Representing Student Performance with Partial Credit (Yutao Wang, Neil T. Heffernan and Joseph E. Beck); (63) Where in the World? Demographic Patterns in Access Data (Mimi M. Recker, Beijie Xu, Sherry Hsi, and Christine Garrard); and (64) Pundit: Intelligent Recommender of Courses (Ankit Ranka, Faisal Anwar, Hui Soo Chae). Individual papers contain tables, figures, footnotes and references

This study aims to compare and contrast the perspectives of preservice teachers and instructors on the "Computer Education and Instructional Technology (CEIT)" department. A questionnaire which was developed from the ideas of CEIT faculty members in the "The Results of Re-Designing the Teacher Education Curricula of the Faculties of Education in Turkish Universities and Teacher Training" Symposium was administered to the CEIT students of Baskent (N=112) and Middle East Technical (N=93) Universities from first to fourth grade students. Under the light of the study results, recommendations are offered for both implication and further studies

Four speeches are presented, each of which deals with the use of conputers to organize and retrieve news stories. The first speech relates in detail the step-by-step process devised by the "Free Press" in Detroit to analyze, categorize, code, film, process, and retrieve news stories through the use of the electronic film retrieval system, Miracode, designed by Eastman Kodak. The second speech discusses the idea of a regional newspaper library which would employ the paper tape used to produce a newspaper to index by computer that day's news stories and to provide a computer-produced microfilm for mounting in member papers' retrieval devices. The third speech discusses the Automated News Clipping, Indexing and Retrieval System (ANCIRS) which uses a minicomputer to control a very high speed microfiche retrieval terminal so that any piece of morgue copy is available to the viewer in approximately four seconds on the Image Systems terminal. The fourth speech concerns the development of a new, automated computer-assisted system, ACCESS, now in use by the "Chicago Daily News" and "Chicago Sun-Times" Editorial Library. (KKC)

Describing cognitive science as a new discipline based upon the integration of elements of psychology, computer science, linguistics, philosophy, and education, this paper argues that such a discipline has important implications for the field of communication. Following an overview of the literature of cognitive science, the paper offers an explication of the computational paradigm underlying the new discipline. It then refutes attacks that have been made upon the paradigm's assumption that artificial intelligence research and psychological experimentation should be mutually heuristic because they both seek a unified theory of the cognitive processes performed by any system capable of certain tasks. In an illustration of the kinds of theoretical concepts that are emerging from cognitive science, the paper describes a recently proposed theory of story comprehension. In conclusion, the paper argues that cognitive science has important implications for communication research in the areas of content analysis methodology, the theory of messages, and the theory of communicative competence, and it outlines a proposed research project designed to illustrate those implications. (Author/FL)

This paper describes an investigation of problem sequencing as part of a symposium presenting theoretical concerns and research findings regarding several diverse types of strategies for designing and presenting instructional materials. The first step involved analyzing a set of problems, to determine the knowledge (rules) required for solving the problems. A set of geometric construction problems was analyzed using a method for deriving and simplifying rules or algorithms for solving the problems. Next, assumptions were adopted concerning how subjects learn and apply rules to solve problems. A computer was programmed according to these assumptions, and given some of the simplified rules identified during the previous step, in order to simulate some aspects of subjects' problem-solving and learning. Various problem sequences were then given to the program, to identify those that were solvable and "learnable." Finally, for initial evaluation of the approach, sequences that were learnable for the program were given to subjects, whose performance was compared with the performance of subjects given random or learner-controlled sequences. (Author/MLF)

This paper states that there are two fundamental reasons for the comparative absence of computer use for parks and recreation at the present time. These are (1) lack of clear cut cost justification and (2) reluctance on the part of recreation professionals to accept their role as managers and, consequently, to utilize modern management tools. The paper attempts to describe the most effective use of the computer and telecommunication technologies for the leisure service provider. It seeks to make the part and recreation professional recognize that the ultimate effective use of the technology will be in the hands of those professionals who combine a basic understanding of their own profession with that of the technology itself. The paper contains a primer on computers for the recreation/park professional. A section on the primary applications of computer technology to recreation and parks is followed by examples of specific application to these areas. More detailed examples are appended. (Author/DDO)