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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.]

"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.]

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.

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.

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.

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)

For the past 15 years there has been a serious interest in the processing of natural language (English) by researchers in Artificial Intelligence (A.I.). This processing has included machine translation, question-answering systems, man-machine dialogue, and speech understanding. This interest has engendered an awareness of and a concern with the ongoing activity in contemporary linguistics. Therefore, it may be of interest to linguistis to discover what has seemed important for A.I. and how it has been adapted and used. Thus a brief history of the relation (almost always one-way) between A.I. and linguistics is presented. Some of the works in A.I. surveyed range from those of the early sixties, such as Lindsay's SAD SAM, Green et al BASEBALL, and Bobrow's STUDENT, to more recent efforts, including Wood's transition network grammars, Winograd's SHRDLU, and Schank's conceptual dependency models. In one way or another, these computer programs and others depend on the work of Chomsky, both "Syntactic Structures" and "Aspects," Halliday's systemic grammar, and some of the ideas of generative semantics as developed by G. Lakoff, McCawley, and Fillmore. (Author/KM)

When a grammar is expressed in augmented transition network (ATN) form, the problem of detecting syntactic ambuguity reduces to finding all possible paths through the ATNs. Each successfully terminating path through the ATN generates an acceptable parsing of the input string. Two ATN forms, minimal-node and pseudo-tree, are described along with the conventions for traversing each. The two forms are compared in regard to efficient use of computer time and space and in regard to appropriateness for each of the three path-finding techniques. Three techniques are discussed for finding all acceptable paths through ATNs. The techniques are "Backtracking,""Simultaneous Parallel Analysis," and "Amputate And Re-enter." Relative merits of the three techniques are discussed in terms of computer execution time, required data storage, programmer time, and amenability of the program to modification. A rudimentary ATN-based parser for English has been written in SPITBOL to test the implementation of these techniques. (Author)

This is a description of the motivation and overall organization of the OWL language for knowledge representation. OWL consists of a linguistic memory system (LMS), a memory of concepts in terms of which all English phrases and all knowledge of an application domain are represented; a theory of English grammar which tells how to map English phrases into concepts; a parser to perform that mapping for individual sentences; and an interpreter to carry out procedures which are written in the same representational formalism. The system has been applied to the study of interactive dialogs, explanations of its own reasoning, and question answering. (Author/AM)

Evaluation models are being developed for assessing artificial intelligence (AI) systems in terms of similar performance by groups of people. Natural language understanding and vision systems are the areas of concentration. In simplest terms, the goal is to norm a given natural language system's performance on a sample of people. The specific program under study is a natural language query system, IRUS--an interface between the user and the information desired. IRUS is designed to serve as a general purpose interface to a broad range of databases and expert systems. A pilot study is discussed, which was conducted with early elementary school and preschool students to determine the appropriate language understanding level at which to administer the IRUS test. In the vision area, common measures of visual tasks are being analyzed in terms of their appropriateness to the vision system. This is the inverse of the language exploration that began with the tasks and created the measures. A review by the vision community of approaches they would use to compare human and machine vision will determine if looking for consistent benchmarks is a feasible approach. (SLD)