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Many students struggle with proof writing. However, struggle is not universally bad: researchers have distinguished between productive and unproductive forms of struggle and have identified productive struggle as essential for learning mathematics. Yet, in practice, recognizing when learners are engaged in productive struggle or unproductive struggle can be challenging. In this report, I argue that students' gesture production may indicate engagement in productive struggle. I observed three undergraduate students from an introductory point-set topology course, collaborating in pairs to complete proof tasks. I present evidence from the students' work on two proof tasks that undergraduate students' gesture frequently when they are engaged in productive struggle and that gesture is rare during engagement in unproductive struggle. [For the complete proceedings, see ED630060.]

Example use in undergraduate mathematics has been extensively studied. However, little is known about undergraduates' example use in the context of introductory point-set topology. We present a case study of one undergraduate and describe her example use when constructing proofs in the context of introductory topology. We describe her use of a type of example previously undiscussed in the research literature, which we call structural examples. We conclude by drawing distinctions between structural examples and other, more specific forms of example.

The 7th International Conference on Education Data Mining held on July 4th-7th, 2014, at the Institute of Education, London, UK is the leading international forum for high-quality research that mines large data sets in order to answer educational research questions that shed light on the learning process. These data sets may come from the traces that students leave when they interact, either individually or collaboratively, with learning management systems, interactive learning environments, intelligent tutoring systems, educational games or when they participate in a data-rich learning context. The types of data therefore range from raw log files to eyetracking devices and other sensor data. Being hosted in London, UK the theme of the conference is "Big Data--Big Ben--Education Data Mining for Big Impact in Teaching and Learning". In this seventh year of EDM conferences, it is clear that the field is continuing to grow at a rapid pace. With renewed focus on education driven by big data learning analytics has put the EDM field in the center of growing interest. Traditional educational technologies, intelligent tutoring systems, educational games, and learning management systems all continue to generate growing amounts of data that are becoming available for analysis. The new interest in MOOCs and their promise to reach thousands or even hundreds of thousands of students per class requires techniques for feedback and grading that are being researched in the EDM domain. The conference submissions this year also continue to grow. A tremendous amount of work has gone into bringing this conference together, and the following are presented: (1) The Field of EDM: Where We Came from and Where We're Going (Joseph Beck); (2) Generative Adaptivity for Optimization of the Learning Ecosystem (Zoran Popovic; (3) 150K+ Online Students at a Time: How to Understand What's Happening in Online 4 Learning (Daniel Russell); (4) Adaptive Practice of Facts in Domains with Varied Prior Knowledge (Jan Papoušek, Radek Pelánek and Vít Stanislav); (5) Alternating Recursive Method for Q-Matrix Learning (Yuan Sun, Shiwei Ye, Shunya Inoue and Yi Sun); (6) Application of Time Decay Functions and the Elo System in Student Modeling (Radek Pelánek); (7) Causal Discovery with Models: Behavior, Affect, and Learning in Cognitive Tutor Algebra (Stephen Fancsali); (8) Choice-Based Assessment: Can Choices Made in Digital Games Predict 6th-Grade Students' Math Test Scores? (Min Chi, Daniel Schwartz, Kristen Pilner Blair and Doris B. Chin); (9) Comparing Expert and Metric-Based Assessments of Association Rule Interestingness (Diego Luna Bazaldua, Ryan Baker and Maria Ofelia San Pedro); (10) Different Parameters - Same Prediction: An Analysis of Learning Curves (Tanja Käser, Kenneth Koedinger and Markus Gross); (11) Discovering Gender-Specific Knowledge from Finnish Basic Education Using PISA Scale Indices (Mirka Saarela and Tommi Kärkkäinen); (12) EduRank: A Collaborative Filtering Approach to Personalization in E-Learning (Avi Segal, Ziv Katzir, Kobi Gal, Guy Shani and Bracha Shapira); (13) Exploring Differences in Problem Solving with Data-Driven Approach Maps (Michael Eagle and Tiffany Barnes); (14) General Features in Knowledge Tracing: Applications to Model Multiple Subskills, Temporal Item Response Theory, and Expert Knowledge (José González-Brenes, Yun Huang and Peter Brusilovsky); (15) Generating Hints for Programming Problems Using Intermediate Output ( Barry Peddycord III, Andrew Hicks and Tiffany Barnes); (16) Integrating Latent-Factor and Knowledge-Tracing Models to Predict Individual Differences in Learning (Mohammad Khajah, Rowan Wing, Robert Lindsey and Michael Mozer); (17) Interpreting Model Discovery and Testing Generalization to a New Dataset (Ran Liu, Elizabeth A. McLaughlin and Kenneth R. Koedinger); (18) Learning Individual Behavior in an Educational Game: A Data-Driven Approach (Seong Jae Lee, Yun-En Liu and Zoran Popovic); (19) Predicting Learning and Affect from Multimodal Data Streams in Task-Oriented Tutorial Dialogue (Joseph Grafsgaard, Joseph Wiggins, Kristy Elizabeth Boyer, Eric Wiebe and James Lester); (20) Sentiment Analysis in MOOC Discussion Forums: What does It Tell Us? (Miaomiao Wen, Diyi Yang and Carolyn Rose); (21) The Effect of Mutual Gaze Perception on Students' Verbal Coordination (Bertrand Schneider and Roy Pea); (22) The Opportunities and Limitations of Scaling Up Sensor-Free Affect Detection (Michael Wixon, Ivon Arroyo, Kasia Muldner, Winslow Burleson, Cecil Lozano and Beverly Woolf); (23) The Problem Solving Genome: Analyzing Sequential Patterns of Student Work with Parameterized Exercises (Julio Guerra, Shaghayegh Sahebi, Peter Brusilovsky and Yu-Ru Lin); (24) Trading Off Scientific Knowledge and User Learning with Multi-Armed Bandits (Yun-En Liu, Travis Mandel, Emma Brunskill and Zoran Popovic); (25) Vertical and Stationary Scales for Progress Maps (Russell Almond, Ilya Goldin, Yuhua Guo and Nan Wang); (26) Visualization and Confirmatory Clustering of Sequence Data from a Simulation- Based Assessment Task (Yoav Bergner, Zhan Shu and Alina von Davier); (27) Who's in Control?: Categorizing Nuanced Patterns of Behaviors within a Game- Based Intelligent Tutoring System (Erica Snow, Laura Allen, Devin Russell and Danielle McNamara); (28) Acquisition of Triples of Knowledge from Lecture Notes: A Natural Language Processing Approach (Thushari Atapattu, Katrina Falkner and Nickolas Falkner); (29) Towards Assessing Students' Prior Knowledge from Tutorial Dialogues (Dan Stefanescu, Vasile Rus and Art Graesser); (30) Assigning Educational Videos at Appropriate Locations in Textbooks (Marios Kokkodis, Anitha Kannan and Krishnaram Kenthapadi) (31) Better Data Beats Big Data (Michael Yudelson, Stephen Fancsali, Steven Ritter, Susan Berman, Tristan Nixon and Ambarish Joshi); (32) Building a Student At-Risk Model: An End-to-End Perspective (Lalitha Agnihotri and Alexander Ott); (33) Can Engagement be Compared? Measuring Academic Engagement for Comparison (Ling Tan, Xiaoxun Sun and Siek Toon Khoo); (34) Comparison of Algorithms for Automatically Building Example-Tracing Tutor Models (Rohit Kumar, Matthew Roy, Bruce Roberts and John Makhoul); (35) Computer-Based Adaptive Speed Tests (Daniel Bengs and Ulf Brefeld); (36) Discovering Students' Complex Problem Solving Strategies in Educational Assessment (Krisztina Tóth, Heiko Rölke, Samuel Greiff and Sascha Wüstenberg); (37) Discovering Theoretically Grounded Predictors of Shallow vs. Deep-Level Learning (Carol Forsyth, Arthur Graesser, Philip I. Pavlik Jr., Keith Millis and Borhan Samei); (38) Domain Independent Assessment of Dialogic Properties of Classroom Discourse (Borhan Samei, Andrew Olney, Sean Kelly, Martin Nystrand, Sidney D'Mello, Nathan Blanchard, Xiaoyi Sun, Marci Glaus and Art Graesser); (39) Empirically Valid Rules for Ill-Defined Domains (Collin Lynch and Kevin Ashley); (40) Entropy: A Stealth Measure of Agency in Learning Environments (Erica Snow, Matthew Jacovina, Laura Allen, Jianmin Dai and Danielle McNamara); (41) Error Analysis as a Validation of Learning Progressions (Brent Morgan, William Baggett and Vasile Rus); (42) Exploration of Student's Use of Rule Application References in a Propositional Logic Tutor (Michael Eagle, Vinaya Polamreddi, Behrooz Mostafavi and Tiffany Barnes); (43) Exploring Real-Time Student Models Based on Natural-Language Tutoring Sessions (Benjamin Nye, Mustafa Hajeer, Carolyn Forsyth, Borhan Samei, Xiangen Hu and Keith Millis); (44) Forum Thread Recommendation for Massive Open Online Courses (Diyi Yang, Mario Piergallini, Iris Howley and Carolyn Rose); (45) Investigating Automated Student Modeling in a Java MOOC (Michael Yudelson, Roya Hosseini, Arto Vihavainen and Peter Brusilovsky); (46) Mining Gap-Fill Questions from Tutorial Dialogues (Nobal B. Niraula, Vasile Rus, Dan Stefanescu and Arthur C. Graesser); (47) Online Optimization of Teaching Sequences with Multi-Armed Bandits (Benjamin Clement, Pierre-Yves Oudeyer, Didier Roy and Manuel Lopes); (48) Predicting MOOC Performance with Week 1 Behavior (Suhang Jiang, Adrienne Williams, Katerina Schenke, Mark Warschauer and Diane O'Dowd); (49) Predicting STEM and Non-STEM College Major Enrollment from Middle School Interaction with Mathematics Educational Software (Maria Ofelia San Pedro, Jaclyn Ocumpaugh, Ryan Baker and Neil Heffernan); (50) Quantized Matrix Completion for Personalized Learning (Andrew Lan, Christoph Studer and Richard Baraniuk); (51) Reengineering the Feature Distillation Process: A Case Study in Detection of Gaming the System (Luc Paquette, Adriana de Carvahlo, Ryan Baker and Jaclyn Ocumpaugh); (52) SKETCHMINER: Mining Learner-Generated Science Drawings with Topological Abstraction (Andy Smith, Eric N. Wiebe, Bradford W. Mott and James C. Lester); (53) Teachers and Students Learn Cyber Security: Comparing Software Quality, Security (Shlomi Boutnaru and Arnon Hershkovitz); (54) Testing the Multimedia Principle in the Real World: A Comparison of Video vs. Text Feedback in Authentic Middle School Math Assignments (Korinn Ostrow and Neil Heffernan); (55) The Importance of Grammar and Mechanics in Writing Assessment and Instruction: Evidence from Data Mining (Scott Crossley, Kris Kyle, Laura Allen and Danielle McNamara); (56) The Long and Winding Road: Investigating the Differential Writing Patterns of High and Low Skilled Writers (Laura Allen, Erica Snow and Danielle McNamara); (57) The Refinement of a Q-Matrix: Assessing Methods to Validate Tasks to Skills Mapping (Michel Desmarais, Behzad Beheshti and Peng Xu); (58) Tracing Knowledge and Engagement in Parallel in an Intelligent Tutoring System (Sarah Schultz and Ivon Arroyo); (59) Tracking Choices: Computational Analysis of Learning Trajectories (Erica Snow, Laura Allen, G.Tanner Jackson and Danielle McNamara); (60) Unraveling Students' Interaction Around a Tangible Interface Using Gesture Recognition (Bertrand Schneider and Paulo Blikstein); (61) A Predictive Model for Video Lectures Classification (Priscylla Silva, Roberth Pinheiro and Evandro Costa); (62) Accepting or Rejecting Students' Self-Grading in their Final Marks by using Data Mining (Javier Fuentes, Cristobal Romero, Carlos García-Martínez and Sebastián Ventura); (63) Analysis and extraction of behaviors by students in lectures 329 Eiji Watanabe, Takashi Ozeki and Takeshi Kohama (64) Analysis of Student Retention and Drop-Out Using Visual Analytics (Jan Géryk and Lubomír Popelínský); (65) Automatic Assessment of Student Reading Comprehension from Short Summaries (Lisa Mintz, Dan Stefanescu, Shi Feng, Sidney D'Mello and Arthur Graesser); (66) Building an Intelligent PAL from the Tutor.com Session Database Phase 1: Data Mining (Donald Morrison, Benjamin Nye, Borhan Samei, Vivek Varma Datla, Craig Kelly and Vasile Rus); (67) Building Automated Detectors of Gameplay Strategies to Measure Implicit Science Learning (Elizabeth Rowe, Ryan Baker, Jodi Asbell-Clarke, Emily Kasman and William Hawkins); (68) Challenges on Applying BKT to Model Student Knowledge in Multi-Context Online Learning Environment (Wolney Mello Neto and Eduardo Barbosa); (69) Combination of Statistical and Semantic Data Sources for the Improvement of Software Engineering Courses (Michael Koch, Markus Ring, Florian Otto and Dieter Landes); (70) Comparing Learning in a MOOC and a Blended On-Campus Course (Kimberly Colvin, John Champaign, Alwina Liu, Colin Fredericks and David Pritchard); (71) Cost-Effective, Actionable Engagement Detection at Scale (Ryan Baker and Jaclyn Ocumpaugh); (72) Data Mining of Undergraduate Course Evaluations (Sohail Javaad Syed, Yuheng Helen Jiang and Lukasz Golab); (73) Data Sharing: Low-Cost Sensors for Affect and Cognition (Keith Brawner) (74) Diagnosing Algebra Understanding via Inverse Planning (Anna Rafferty and Thomas Griffiths); (75) Discovering and Describing Types of Mathematical Errors (Thomas McTavish and Johann Larusson); (76) Discovering Prerequisite Relationships Among Knowledge Components (Richard Scheines, Elizabeth Silver and Ilya Goldin); (77) Dynamic Re-Composition of Learning Groups Using PSO-Based Algorithms (Zhilin Zheng and Niels Pinkwart); (78) Educational Data Mining and Analyzing of Student Learning Outcomes from the Perspective of Learning Experience (Zhongmei Shu, Qiong-Fei Qu and Lu-Qi Feng); (79) Using EEG in Knowledge Tracing (Yanbo Xu, Kai-Min Chang, Yueran Yuan and Jack Mostow); (80) Exploring Engaging Dialogues in Video Discussions (I-Han Hsiao, Hui Soo Chae, Manav Malhotra, Ryan Baker and Gary Natriello); (81) Exploring Indicators from Keyboard and Mouse Interactions to Predict the User Affective State (Sergio Salmeron-Majadas, Olga C. Santos and Jesus G. Boticario); (82) Extracting Latent Skills from Time Series of Asynchronous and Incomplete Examinations (Shinichi Oeda, Yu Ito and Kenji Yamanishi); (83) Generalizing and Extending a Predictive Model for Standardized Test Scores Based On Cognitive Tutor Interactions (Ambarish Joshi, Stephen Fancsali, Steven Ritter, Tristan Nixon and Susan Berman); (84) How Patterns in Source Codes of Students Can Help in Detection of Their Programming Skills? (Štefan Pero and Tomáš Horváth); (85) A Preliminary Investigation of Learner Characteristics for Unsupervised Dialogue Act Classification (Aysu Ezen-Can and Kristy Elizabeth Boyer); (86) Improving Retention Performance Prediction with Prerequisite Skill Features (Xiaolu Xiong, Seth Adjei and Neil Heffernan); (87) Indicator Visualization for Adaptive Exploratory Learning Environments (Sergio Gutierrez Santos, Manolis Mavrikis, Alex Poulovassilis and Zheng Zhu); (88) Learning Aid Use Patterns and Their Impact on Exam Performance in Online Developmental Mathematics (Nicole Forsgren Velasquez, Ilya Goldin, Taylor Martin and Jason Maughan); (89) Learning to Teach like a Bandit (Mykola Pechenizkiy and Pedro A. Toledo); (90) Matching Hypothesis Text in Diagrams and Essays (Collin Lynch, Mohammad Falakmasir and Kevin Ashley); (91) Matrix Factorization Feasibility for Sequencing and Adaptive Support in Intelligent Tutoring Systems (Carlotta Schatten, Ruth Janning, Manolis Mavrikis and Lars Schmidt-Thieme); (92) Microgenetic Designs for Educational Data Mining Research (Taylor Martin, Nicole Forsgren Velasquez, Ani Aghababyan, Jason Maughan and Philip Janisiewicz); (93) Mining and Identifying Relationships among Sequential Patterns in Multi-Feature, Hierarchical Learning Activity Data (Cheng Ye, John Kinnebrew and Gautam Biswas); (94) Mining Coherent Evolution Patterns in Education through Biclustering (André Vale, Sara C. Madeira and Claudia Antunes); (95) Mining Multi-Dimensional Patterns for Student Modelling (Andreia Silva and Claudia Antunes); (96) Mining Reading Comprehension Within Educational Objective Frameworks (Terry Peckham and Gordon McCalla); (97) Mining Students' Strategies to enable Collaborative Learning (Sergio Gutierrez-Santos, Manolis Mavrikis and Alexandra Poulovassilis); (98) Modeling Student Socioaffective Responses to Group Interactions in a Collaborative Online Chat Environment (Whitney Cade, Nia Dowell, Art Graesser, Yla Tausczik and James Pennebaker); (99) Now We're Talkin': Leveraging the Power of Natural Language Processing to Inform ITS Development (Laura Allen, Erica Snow and Danielle McNamara); (100) Peer Assessment in the First French MOOC: Analyzing Assessors' Behavior (Matthieu Cisel, Rémi Bachelet and Eric Bruillard); (101) Peer Influence on Attrition in Massively Open Online Courses (Diyi Yang, Miaomiao Wen and Carolyn Rose); (102) Predicting Students' Learning Achievement by Using Online Learning Patterns in Blended Learning Environments: Comparison of Two Cases on Linear and Non-Linear Model (Jeong Hyun Kim, Yeonjeong Park, Jongwoo Song and Il-Hyun Jo); (103) Predictive Performance of Prevailing Approaches to Skills Assessment Techniques: Insights from Real vs. Synthetic Data Sets (Behzad Beheshti and Michel Desmarais); (104) Recent-Performance Factors Analysis (April Galyardt and Ilya Goldin); (105) Refining Learning Maps with Data Fitting Techniques: Searching for Better Fitting Learning Maps (Seth Adjei, Douglas Selent, Neil Heffernan, Zach Pardos, Angela Broaddus and Neal Kingston); (106) Relevancy Prediction of Micro-Blog Questions in an Educational Setting (Mariheida Cordova-Sanchez, Parameswaran Raman, Luo Si and Jason Fish); (107) Singular Value Decomposition in Education: A Case Study on Recommending Courses (Fábio Carballo and Claudia Antunes); (108) The Predictive Power of SNA Metrics in Education (Diego García-Saiz, Camilo Palazuelos and Marta Zorrilla); (109) Data-Driven Curriculum Design: Mining the Web to Make Better Teaching Decisions (Antonio Moretti, Jose Gonzalez-Brenes and Katherine McKnight); (110) Towards IRT-Based Student Modeling from Problem Solving Steps (Manuel Hernando, Eduardo Guzmán, Sergey Sosnovsky, Eric Andres and Susanne Narciss); (111) Towards Uncovering the Mysterious World of Math Homework (Mingyu Feng); (112) Towards Using Similarity Measure for Automatic Detection of Significant Behaviors from Continuous Data (Ben-Manson Toussaint, Vanda Luengo and Jérôme Tonetti); (113) Using Data Mining to Automate ADDIE (Fritz Ray, Keith Brawner and Robby Robson); (114) Using Multimodal Learning Analytics to Study Learning Mechanisms in Hands-on Environments (Marcelo Worsley and Paulo Blikstein); (115) Using Problem Solving Times and Expert Opinion to Detect Skills (Juraj Nižnan, Radek Pelánek and Jirí Rihák); (116) Toward Collaboration Sensing: Multimodal Detection of the Chameleon Effect in Collaborative Learning Settings (Bertrand Schneider); (117) The Use of Student Confidence for Prediction & Resolving Individual Student Knowledge Structure (Charles Lang); (118) Nonverbal Communication and Teaching Performance (Roghayeh Barmaki); (119) Data-Driven Feedback Beyond Next-Step Hints (Michael Eagle and Tiffany Barnes); (120) E3: Emotions, Engagement and Educational Games (Ani Aghababyan); (121) MOOC Leaner Motivation and Learning Pattern Discovery--A Research Prospectus Paper (Yuan Wang); and (122) Personalization and Incentive Design in E-learning Systems (Avi Segal). Workshops presented include: (1) Graph-based Educational Data Mining (G-EDM) (Collin F. Lynch, Tiffany Barnes); (2) Non-Cognitive Factors & Personalization for Adaptive Learning (NCFPAL@EDM) (Steven Ritter, Stephen E. Fancsali); (3) Approaching Twenty Years of Knowledge Tracing: Lessons Learned, Open Challenges, and Promising Developments (Michael Yudelson, José P. González-Brenes, Michael Mozer); and (4) Feedback from Multimodal Interaction in Learning Management Systems (Lars Schmidt-Thieme, Arvid Kappas, Carles Sierra, Emanuele Ruffaldi). References are included in each presentation.

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In continuation and development of our previous works where nuclear reactions of moderate energy (10-400 MeV) protons with Si, Al and W have been investigated, the results of reactions with Cu are reported in this paper. Cu is a most important component in composition of materials in contact pads and pathways of modern and perspective ultra large-scale integration circuitry, especially in 3D topology. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a digital H-space, a digital H-group and a digital H-map between digital H-spaces. We give definition of digital H-space and then its basic properties. We give an example which is a digital H-space. Some conclusions about digital H-spaces are associated with algebraic topology. Furthermore we obtained some results about digital H-spaces. At the end of, the importance of digital H-space in digital topology is discussed. [ABSTRACT FROM AUTHOR]

The contributions to the D0 − D ¯ 0 $ \bar {D}^0 $ mixing parameter y from the D0 → PP, PV channels are calculated, based on the corresponding decay amplitudes extracted from data in the factorization-assisted topological-amplitude approach. It is found that the PP, PV contributions amount up to yPP+PV = (0.21 ± 0.07), about one third of the experiment measurement yexp = (0.63 ± 0.08)%. [ABSTRACT FROM AUTHOR]