Tiffany Barnes, Min Chi, Mingyu Feng
Proceedings of the 9th International Conference on Educational Data Mining
EDM, 2016.
Contents (153 items)
- EDM-2016-KhajahLM #how #question
- How Deep is Knowledge Tracing? (MK, RVL, MM).
- EDM-2016-KhajahLM_ #how #question
- How Deep is Knowledge Tracing? (MK, RVL, MM).
- EDM-2016-LabutovS #self
- Calibrated Self-Assessment (IL, CS).
- EDM-2016-LabutovS_ #self
- Calibrated Self-Assessment (IL, CS).
- EDM-2016-Agrawal #challenge #data-driven #education
- Data-Driven Education: Some opportunities and Challenges (RA0), p. 2.
- EDM-2016-Linn #learning
- WISE Ways to Strengthen Inquiry Science Learning (MCL), p. 3.
- EDM-2016-Kay #learning #people
- Enabling people to harness and control EDM for lifelong, life-wide learning (JK), p. 4.
- EDM-2016-AgrawalGP #data-driven #design #education #towards
- Toward Data-Driven Design of Educational Courses: A Feasibility Study (RA0, BG, EEP), p. 6.
- EDM-2016-SweeneyLRJ #approach #performance #predict #recommendation #student
- Next-Term Student Performance Prediction: A Recommender Systems Approach (MS, JL, HR, AJ), p. 7.
- EDM-2016-YangKR #online #student
- Exploring the Effect of Student Confusion in Massive Open Online Courses (DY, REK, CPR), p. 8.
- EDM-2016-Kay16a #learning #people
- Enabling people to harness and control EDM for lifelong, life-wide learning (JK), pp. 10–20.
- EDM-2016-AllenJDRKLM #analysis #process
- {ENTER}ing the Time Series {SPACE}: Uncovering the Writing Process through Keystroke Analyses (LKA, MEJ, MD, RDR, KK, ADL, DSM), pp. 22–29.
- EDM-2016-MillsBWD #automation #detection
- Automatic Gaze-Based Detection of Mind Wandering during Film Viewing (CM, RB, XW, SKD), pp. 30–37.
- EDM-2016-ChenLXSMD #multimodal #problem #process
- Riding an emotional roller-coaster: A multimodal study of young child's math problem solving activities (LC, XL, ZX, ZS, LPM, AD), pp. 38–45.
- EDM-2016-ChenGT #graph #modelling #student
- Joint Discovery of Skill Prerequisite Graphs and Student Models (YC, JPGB, JT0), pp. 46–53.
- EDM-2016-DavisCHH #learning
- Gauging MOOC Learners' Adherence to the Designed Learning Path (DD, GC, CH, GJH), pp. 54–61.
- EDM-2016-AlfaroS #empirical
- Dynamics of Peer Grading: An Empirical Study (LdA, MS), pp. 62–69.
- EDM-2016-DoroudiHAB16a #how #matter #process #sequence
- Sequence Matters, But How Exactly? A Method for Evaluating Activity Sequences from Data (SD, KH, VA, EB), pp. 70–77.
- EDM-2016-HicksLEB #education #game studies
- Measuring Gameplay Affordances of User-Generated Content in an Educational Game (AH, ZL, ME, TB), pp. 78–85.
- EDM-2016-HicksLEB_ #education #game studies
- Measuring Gameplay Affordances of User-Generated Content in an Educational Game (AH, ZL, ME, TB), pp. 78–85.
- EDM-2016-HuttMWDD #detection #learning
- The Eyes Have It: Gaze-based Detection of Mind Wandering during Learning with an Intelligent Tutoring System (SH, CM, SW, PJD, SKD), pp. 86–93.
- EDM-2016-KlinglerKSG #clustering #student
- Temporally Coherent Clustering of Student Data (SK, TK, BS, MHG), pp. 102–109.
- EDM-2016-LabutovL #learning #web
- Web as a textbook: Curating Targeted Learning Paths through the Heterogeneous Learning Resources on the Web (IL, HL), pp. 110–118.
- EDM-2016-LiuBLBBBM #analysis #behaviour
- MOOC Learner Behaviors by Country and Culture; an Exploratory Analysis (ZL, RB, CL, TB, RSB, YB, DSM), pp. 127–134.
- EDM-2016-MaABM #student
- Effect of student ability and question difficulty on duration (YM, LA, RSB, SM), pp. 135–142.
- EDM-2016-MaassP #modelling #retrieval
- Modeling the Influence of Format and Depth during Effortful Retrieval Practice (JKM, PIPJ), pp. 143–150.
- EDM-2016-MacLellanHPK #architecture #education #learning
- The Apprentice Learner architecture: Closing the loop between learning theory and educational data (CJM, EH, RP, KRK), pp. 151–158.
- EDM-2016-McBroomJKY #behaviour #mining #student
- Mining behaviors of students in autograding submission system logs (JM, BJ, IK, KY), pp. 159–166.
- EDM-2016-NgHLK #learning #modelling #sequence #using
- Modelling the way: Using action sequence archetypes to differentiate learning pathways from learning outcomes (KHRN, KH, KL, AWHK), pp. 167–174.
- EDM-2016-NiuNZWKY #algorithm #clustering #learning
- A Coupled User Clustering Algorithm for Web-based Learning Systems (KN, ZN, XZ, CW, KK, MY), pp. 175–182.
- EDM-2016-PaassenJH #data transformation #execution #programming #representation
- Execution Traces as a Powerful Data Representation for Intelligent Tutoring Systems for Programming (BP, JJ, BH), pp. 183–190.
- EDM-2016-PriceDB #data-driven #generative #programming
- Generating Data-driven Hints for Open-ended Programming (TWP, YD, TB), pp. 191–198.
- EDM-2016-RauMN16a #how #learning #similarity #visual notation
- How to Model Implicit Knowledge? Similarity Learning Methods to Assess Perceptions of Visual Representations (MAR, BM, RDN), pp. 199–206.
- EDM-2016-RauMN16a_ #how #learning #similarity #visual notation
- How to Model Implicit Knowledge? Similarity Learning Methods to Assess Perceptions of Visual Representations (MAR, BM, RDN), pp. 199–206.
- EDM-2016-SalesWP #algebra #predict #student
- Student Usage Predicts Treatment Effect Heterogeneity in the Cognitive Tutor Algebra I Program (AS, AW, JP), pp. 207–214.
- EDM-2016-SharmaBGPD16a #education #multimodal #named #network #predict
- LIVELINET: A Multimodal Deep Recurrent Neural Network to Predict Liveliness in Educational Videos (AS, AB, AG, SP, OD), pp. 215–222.
- EDM-2016-SharmaBGPD16a_ #education #multimodal #named #network #predict
- LIVELINET: A Multimodal Deep Recurrent Neural Network to Predict Liveliness in Educational Videos (AS, AB, AG, SP, OD), pp. 215–222.
- EDM-2016-SlaterOBSIH #learning #problem #semantics #student
- Semantic Features of Math Problems: Relationships to Student Learning and Engagement (SS, JO, RSB, PS, PSI, NTH), pp. 223–230.
- EDM-2016-StapelZP #learning #online #performance #predict #student
- An Ensemble Method to Predict Student Performance in an Online Math Learning Environment (MS, ZZ, NP), pp. 231–238.
- EDM-2016-TomkinsRG #behaviour #case study #performance #predict #student
- Predicting Post-Test Performance from Student Behavior: A High School MOOC Case Study (ST, AR, LG), pp. 239–246.
- EDM-2016-VailWGBWL #predict
- The Affective Impact of Tutor Questions: Predicting Frustration and Engagement (AKV, JBW, JFG, KEB, ENW, JCL), pp. 247–254.
- EDM-2016-XueLC #diagrams #evolution #graph grammar #re-engineering
- Unnatural Feature Engineering: Evolving Augmented Graph Grammars for Argument Diagrams (LX, CL, MC), pp. 255–262.
- EDM-2016-al-RifaieYd #performance #predict
- Investigating Swarm Intelligence for Performance Prediction (MMaR, MYK, Md), pp. 264–269.
- EDM-2016-AshenafiRR #predict #student
- Predicting Student Progress from Peer-Assessment Data (MMA, MR, GR), pp. 270–275.
- EDM-2016-AtapattuFT #approach #classification #topic #visual notation
- Topic-wise Classification of MOOC Discussions: A Visual Analytics Approach (TA, KF, HT), pp. 276–281.
- EDM-2016-BhartiyaCBSM #documentation #learning #segmentation
- Document Segmentation for Labeling with Academic Learning Objectives (DB, DC, SB, BS, MKM), pp. 282–287.
- EDM-2016-BlanchardDOSKSW #automation #detection #education
- Semi-Automatic Detection of Teacher Questions from Human-Transcripts of Audio in Live Classrooms (NB, PJD, AO, BS, SK, XS, BW, MN, SKD), pp. 288–291.
- EDM-2016-BotelhoAH #interactive #modelling #predict
- Modeling Interactions Across Skills: A Method to Construct and Compare Models Predicting the Existence of Skill Relationships (AFB, SA, NTH), pp. 292–297.
- EDM-2016-BoyerV #modelling #predict #robust
- Robust Predictive Models on MOOCs : Transferring Knowledge across Courses (SB, KV), pp. 298–305.
- EDM-2016-Bydzovska #analysis #comparative #performance #predict #student
- A Comparative Analysis of Techniques for Predicting Student Performance (HB), pp. 306–311.
- EDM-2016-Bydzovska16a #recommendation
- Course Enrollment Recommender System (HB), pp. 312–317.
- EDM-2016-ChaplotYCK #automation #data-driven #graph #induction
- Data-driven Automated Induction of Prerequisite Structure Graphs (DSC, YY, JGC, KRK), pp. 318–323.
- EDM-2016-ChoiLHLRW #data-driven #interactive #learning
- Exploring Learning Management System Interaction Data: Combining Data-driven and Theory-driven Approaches (HC, JEL, WJH, KL, MR, AW), pp. 324–329.
- EDM-2016-ClementOL #automation #comparison #education #student
- A Comparison of Automatic Teaching Strategies for Heterogeneous Student Populations (BC, PYO, ML0), pp. 330–335.
- EDM-2016-CrossleyKDM #assessment #automation
- Automatic Assessment of Constructed Response Data in a Chemistry Tutor (SAC, KK, JLD, DSM), pp. 336–340.
- EDM-2016-CutumisuS #assessment #feedback #game studies #learning
- Choosing versus Receiving Feedback: The Impact of Feedback Valence on Learning in an Assessment Game (MC, DLS), pp. 341–346.
- EDM-2016-DaiAY #analysis #learning #recommendation #towards
- Course Content Analysis: An Initiative Step toward Learning Object Recommendation Systems for MOOC Learners (YD, YA, MY), pp. 347–352.
- EDM-2016-DillonBCWASD #student
- Student Emotion, Co-occurrence, and Dropout in a MOOC Context (JD, NB, MC, NW, GAA, BS, SKD), pp. 353–357.
- EDM-2016-FauconKD #markov #simulation #student
- Semi-Markov model for simulating MOOC students (LF, LK, PD), pp. 358–363.
- EDM-2016-FengRMB #difference #gender
- Investigating Gender Difference on Homework in Middle School Mathematics (MF, JR, CM, RB), pp. 364–369.
- EDM-2016-FouhFHKS #data analysis #data type #topic #using
- Investigating Difficult Topics in a Data Structures Course Using Item Response Theory and Logged Data Analysis (EF, MFF, SH, KHK, CAS), pp. 370–375.
- EDM-2016-GelmanRDVJ #behaviour #comparison
- Acting the Same Differently: A Cross-Course Comparison of User Behavior in MOOCs (BUG, MR, CD, KV, AJ), pp. 376–381.
- EDM-2016-HaoLDKK #analysis #collaboration #data mining #mining #problem #statistics
- Collaborative Problem Solving Skills versus Collaboration Outcomes: Findings from Statistical Analysis and Data Mining (JH, LL, AvD, PCK, CK), pp. 382–387.
- EDM-2016-InventadoSIOHOB
- Hint Availability Slows Completion Times in Summer Work (PSI, PS, EVI, KO, NHI, JO, RSB, SS, MA), pp. 388–393.
- EDM-2016-JiangG #approach #contest #graph #mining #on the
- On Competition for Undergraduate Co-op Placements: A Graph Mining Approach (YHJ, LG), pp. 394–399.
- EDM-2016-JoTFRG #behaviour #learning #modelling #social
- Expediting Support for Social Learning with Behavior Modeling (YJ, GT, OF, CPR, DG), pp. 400–405.
- EDM-2016-KidzinskiSBD #modelling #on the
- On generalizability of MOOC models (LK, KS, MSB, PD), pp. 406–411.
- EDM-2016-KoedingerM #analysis
- Closing the Loop with Quantitative Cognitive Task Analysis (KRK, EAM), pp. 412–417.
- EDM-2016-LabartheBBY #question #recommendation #student
- Does a Peer Recommender Foster Students' Engagement in MOOCs? (HL, FB, RB, KY), pp. 418–423.
- EDM-2016-LanB #framework #learning #personalisation
- A Contextual Bandits Framework for Personalized Learning Action Selection (ASL, RGB), pp. 424–429.
- EDM-2016-LiCG #crowdsourcing #how #summary
- How Good Is Popularity? Summary Grading in Crowdsourcing (HL, ZC, ACG), pp. 430–435.
- EDM-2016-LiuDS #data type #data-driven #multi #towards #using
- Beyond Log Files: Using Multi-Modal Data Streams Towards Data-Driven KC Model Improvement (RL0, JLD, JCS), pp. 436–441.
- EDM-2016-LuH #question #scalability
- Seeking Programming-related Information from Large Scaled Discussion Forums, Help or Harm? (YL, SIHH), pp. 442–447.
- EDM-2016-MalkiewichBSKP #behaviour #education #game studies #problem
- Classifying behavior to elucidate elegant problem solving in an educational game (LM, RSB, VS, SK, LP), pp. 448–453.
- EDM-2016-MinWPVBMFWL #interactive #multimodal #predict #student
- Predicting Dialogue Acts for Intelligent Virtual Agents with Multimodal Student Interaction Data (WM, JBW, LP, AKV, KEB, BWM, MF, ENW, JCL), pp. 454–459.
- EDM-2016-MostafaviB #data-driven #logic #problem #student
- Exploring the Impact of Data-driven Tutoring Methods on Students' Demonstrative Knowledge in Logic Problem Solving (BM, TB), pp. 460–465.
- EDM-2016-PelanekR #education #online
- Properties and Applications of Wrong Answers in Online Educational Systems (RP, JR), pp. 466–471.
- EDM-2016-RaffertyJG #feedback #personalisation #using
- Using Inverse Planning for Personalized Feedback (ANR, RJ, TLG), pp. 472–477.
- EDM-2016-Rau #concept #learning #mining #physics #social
- Pattern mining uncovers social prompts of conceptual learning with physical and virtual representations (MAR), pp. 478–483.
- EDM-2016-RenRJ #modelling #multi #performance #predict #using
- Predicting Performance on MOOC Assessments using Multi-Regression Models (ZR, HR, AJ), pp. 484–489.
- EDM-2016-RoweAEHBBE #game studies #learning #metric #validation
- Validating Game-based Measures of Implicit Science Learning (ER, JAC, ME, AH, TB, RB, TE), pp. 490–495.
- EDM-2016-RusGSSG #design #student
- Assessing Student-Generated Design Justifications in Virtual Engineering Internships (VR, DG, ZS, DWS, AG), pp. 496–501.
- EDM-2016-SahebiLB #modelling #performance #predict #student
- Tensor Factorization for Student Modeling and Performance Prediction in Unstructured Domain (SS, YRL, PB), pp. 502–506.
- EDM-2016-ShenC #feature model #learning #modelling
- Aim Low: Correlation-based Feature Selection for Model-based Reinforcement Learning (SS, MC), pp. 507–512.
- EDM-2016-SunY #community #learning #online #personalisation
- Personalization of Learning Paths in Online Communities of Creators (MS, SY), pp. 513–516.
- EDM-2016-TissenbaumBK #behaviour #game studies #markov #modelling #visitor
- Modeling Visitor Behavior in a Game-Based Engineering Museum Exhibit with Hidden Markov Models (MT, MB, VK), pp. 517–522.
- EDM-2016-Sande #component #learning #multi #problem
- Learning Curves for Problems with Multiple Knowledge Components (BvdS), pp. 523–526.
- EDM-2016-WangC #identification #online #student
- A Nonlinear State Space Model for Identifying At-Risk Students in Open Online Courses (FW0, LC0), pp. 527–532.
- EDM-2016-WenMWDHR #collaboration #integration #learning #online #predict
- Transactivity as a Predictor of Future Collaborative Knowledge Integration in Team-Based Learning in Online Courses (MW, KM, XW0, SD, JDH, CPR), pp. 533–538.
- EDM-2016-WilsonKHE #estimation #network
- Back to the basics: Bayesian extensions of IRT outperform neural networks for proficiency estimation (KHW, YK, BH, CE), pp. 539–544.
- EDM-2016-XiongZIB
- Going Deeper with Deep Knowledge Tracing (XX, SZ, EVI, JB), pp. 545–550.
- EDM-2016-XuD #refinement
- Boosted Decision Tree for Q-matrix Refinement (PX, MCD), pp. 551–555.
- EDM-2016-Yudelson16a #parametricity #question #student
- Individualizing Bayesian Knowledge Tracing. Are Skill Parameters More Important Than Student Parameters? (MY), pp. 556–561.
- EDM-2016-ZhangSC #automation #clustering #effectiveness #learning #modelling #student
- Deep Learning + Student Modeling + Clustering: a Recipe for Effective Automatic Short Answer Grading (YZ, RS, MC), pp. 562–567.
- EDM-2016-BaikadiELS #analysis #what
- Redefining “What” in Analyses of Who Does What in MOOCs (AB, CDE, YL, CS), pp. 569–570.
- EDM-2016-BhatnagarDLC #classification #physics #self #student
- Text Classification of Student Self-Explanations in College Physics Questions (SB, MCD, NL, ESC), pp. 571–572.
- EDM-2016-BorgeR #automation #case study #collaboration #experience #feedback #process #quality
- Automated Feedback on the Quality of Collaborative Processes: An Experience Report (MB, CPR), pp. 573–574.
- EDM-2016-BuffumFBWML #assessment #collaboration #embedded #learning #mining #sequence
- Mining Sequences of Gameplay for Embedded Assessment in Collaborative Learning (PSB, MF, KEB, ENW, BWM, JCL), pp. 575–576.
- EDM-2016-CaiLHG #documentation #question #word
- Can Word Probabilities from LDA be Simply Added up to Represent Documents? (ZC, HL, XH, AG), pp. 577–578.
- EDM-2016-ChenDP #diagrams #problem #using
- Examining the necessity of problem diagrams using MOOC AB experiments (ZC, ND, DEP), pp. 579–580.
- EDM-2016-CraigHXFH #behaviour #identification #learning #persistent #predict
- Identifying relevant user behavior and predicting learning and persistence in an ITS-based afterschool program (SDC, XH, JX, YF, XH), pp. 581–582.
- EDM-2016-DianaESK #learning #metric #self #student
- Extracting Measures of Active Learning and Student Self-Regulated Learning Strategies from MOOC Data (ND, ME, JCS, KRK), pp. 583–584.
- EDM-2016-DibieSMQ #community #learning #online #social
- Exploring Social Influence on the Usage of Resources in an Online Learning Community (OD, TS, KEM, DQ), pp. 585–586.
- EDM-2016-Dobashi #education #monitoring #student
- Time Series Cross Section method for monitoring students' page views of course materials and improving classroom teaching (KD), pp. 587–588.
- EDM-2016-DominguezBU #learning #modelling #predict
- Predicting STEM Achievement with Learning Management System Data: Prediction Modeling and a Test of an Early Warning System (MD, MLB, PMU), pp. 589–590.
- EDM-2016-DongKB #comparison #learning #mining #multi #process
- Comparison of Selection Criteria for Multi-Feature Hierarchical Activity Mining in Open Ended Learning Environments (YD, JSK, GB), pp. 591–592.
- EDM-2016-HuangGB #data-driven #framework #modelling
- A Data-Driven Framework of Modeling Skill Combinations for Deeper Knowledge Tracing (YH0, JG, PB), pp. 593–594.
- EDM-2016-JiangLZL #concept #generative #semantics
- Generating Semantic Concept Map for MOOCs (ZJ, PL, YZ, XL), pp. 595–596.
- EDM-2016-JoL #how #learning #online
- How to Judge Learning on Online Learning: Minimum Learning Judgment System (JJ, HL), pp. 597–598.
- EDM-2016-JugoKS #student #towards
- Guiding Students Towards Frequent High-Utility Paths in an Ill-Defined Domain (IJ, BK, VS), pp. 599–600.
- EDM-2016-LambHUP #pointer
- Portrait of an Indexer - Computing Pointers Into Instructional Videos (AL, JH, JDU, AP), pp. 601–602.
- EDM-2016-LeeRBY #analysis #approach #clustering #heatmap #interactive #learning #visualisation
- Hierarchical Cluster Analysis Heatmaps and Pattern Analysis: An Approach for Visualizing Learning Management System Interaction Data (JEL, MR, AB, MY), pp. 603–604.
- EDM-2016-LiB #comprehension
- Understanding Engagement in MOOCs (QL, RB), pp. 605–606.
- EDM-2016-MatsudaCS #detection #how #question
- How quickly can wheel spinning be detected? (NM, SC, JCS), pp. 607–608.
- EDM-2016-McBroomJKY16a #student
- Exploring and Following Students' Strategies When Completing Their Weekly Tasks (JM, BJ, IK, KY), pp. 609–610.
- EDM-2016-MoriC #behaviour #identification #online #performance #student
- Identifying Student Behaviors Early in the Term for Improving Online Course Performance (MM, PC), pp. 611–612.
- EDM-2016-MlynarskaCG #analysis #process
- Time Series Analysis of VLE Activity Data (EM, PC, DG), pp. 613–614.
- EDM-2016-Nixon #scalability
- Massively Scalable EDM with Spark (TN), p. 615.
- EDM-2016-NogaitoYK #automation #case study #similarity #testing #using
- Study on Automatic Scoring of Descriptive Type Tests using Text Similarity Calculations (IN, KY, HK), pp. 616–617.
- EDM-2016-QuigleyDSHPSAP #learning
- Equity of Learning Opportunities in the Chicago City of Learning Program (DQ, OD, MAS, KVH, WRP, TS, UA, NP), pp. 618–619.
- EDM-2016-RamanarayananK #behaviour #collaboration #novel #problem
- Novel features for capturing cooccurrence behavior in dyadic collaborative problem solving tasks (VR, SK), pp. 620–621.
- EDM-2016-RauP #modelling #predict #representation
- Adding eye-tracking AOI data to models of representation skills does not improve prediction accuracy (MAR, ZAP), pp. 622–623.
- EDM-2016-RitterF #generative
- MATHia X: The Next Generation Cognitive Tutor (SR, SF), pp. 624–625.
- EDM-2016-RitterYFB #automation #towards
- Towards Integrating Human and Automated Tutoring Systems (SR, MY, SF, SRB), pp. 626–627.
- EDM-2016-RoscoeJAJM #automation #evaluation #feedback #towards
- Toward Revision-Sensitive Feedback in Automated Writing Evaluation (RDR, MEJ, LKA, ACJ, DSM), pp. 628–629.
- EDM-2016-RusBMMRY #classification #online #tutorial
- Preliminary Results On Dialogue Act and Subact Classification in Chat-based Online Tutorial Dialogues (VR, RB, NM, DMM, SR, MY), pp. 630–631.
- EDM-2016-SabourinMW #data mining #education #mining #tool support
- SAS Tools for Educational Data Mining (JS, SWM, ADW), pp. 632–633.
- EDM-2016-Sherzad #challenge #data mining #education #mining
- Applicability of Educational Data Mining in Afghanistan: Opportunities and Challenges (ARS), pp. 634–635.
- EDM-2016-ShimadaOO #matrix #mining
- Browsing-Pattern Mining from e-Book Logs with Non-negative Matrix Factorization (AS, FO, HO), pp. 636–637.
- EDM-2016-BoroujeniKD #how #question
- How employment constrains participation in MOOCs? (MSB, LK, PD), pp. 638–639.
- EDM-2016-SnowKPFB #how #learning #online #performance #student
- Quantifying How Students Use an Online Learning System: A Focus on Transitions and Performance (ELS, AEK, TEP, MF, AJB), pp. 640–641.
- EDM-2016-StanhopeR #education #framework #platform
- A Platform for Integrating and Analyzing Data to Evaluate the Impacts of Educational Technologies (DS, KR), pp. 642–643.
- EDM-2016-StanhopeR16a #education #what
- Educational Technology: What 49 Schools Discovered about Usage when the Data were Uncovered (DS, KR), pp. 644–645.
- EDM-2016-Sande16a #analysis #component #learning #problem
- Learning curves versus problem difficulty: an analysis of the Knowledge Component picture for a given context (BvdS), pp. 646–647.
- EDM-2016-WhitmerDO #automation #validation
- Validating Automated Triggers and Notifications @ Scale in Blackboard Learn (JW, AD, BO), pp. 648–649.
- EDM-2016-WilliamsBSHL
- Discovering 'Tough Love' Interventions Despite Dropout (JJW, AB, AS, NTH, CL), pp. 650–651.
- EDM-2016-Yee-Kingd #collaboration #learning #markov #online #process #social
- Stimulating collaborative activity in online social learning environments with Markov decision processes (MYK, Md), pp. 652–653.
- EDM-2016-Yee-KingGd #collaboration #learning #metric #online #predict #social #student #using
- Predicting student grades from online, collaborative social learning metrics using K-NN (MYK, AGR, Md), pp. 654–655.
- EDM-2016-GonzalezMRM #case study #collaboration #predict
- Meta-learning for predicting the best vote aggregation method: Case study in collaborative searching of LOs (AZG, VHM, CR, MEPM), pp. 656–657.
- EDM-2016-ZhengKTG #clustering #concept #physics #using
- Soft Clustering of Physics Misconceptions Using a Mixed Membership Model (GZ, SK, YT, AG), pp. 658–659.
- EDM-2016-ZhengSP #exclamation #learning #student
- Perfect Scores Indicate Good Students !? The Case of One Hundred Percenters in a Math Learning System (ZZ, MS, NP), pp. 660–661.
- EDM-2016-BarmakiH #comprehension #coordination #education #gesture #towards
- Towards the Understanding of Gestures and Vocalization Coordination in Teaching Context (RB, CEH), pp. 663–665.
- EDM-2016-HuangB #framework #learning #modelling #student #towards
- Towards Modeling Chunks in a Knowledge Tracing Framework for Students' Deep Learning (YH0, PB), pp. 666–668.
- EDM-2016-Kyrilov #automation #feedback #programming #reasoning #using
- Using Case-Based Reasoning to Automatically Generate High-Quality Feedback for Programming Exercises (AK), pp. 669–671.
- EDM-2016-Nam #adaptation #behaviour #learning #predict
- Predicting Off-task Behaviors for Adaptive Vocabulary Learning System (SN), pp. 672–674.
- EDM-2016-Penteado #assessment #data mining #estimation #mining #scalability #semantics #using
- Estimation of prerequisite skills model from large scale assessment data using semantic data mining (BEP), pp. 675–677.
- EDM-2016-Wang #concept #design #interactive #learning #personalisation
- Designing Interactive and Personalized Concept Mapping Learning Environments (SW0), pp. 678–680.
- EDM-2016-EmondBGG
- Analysing and Refining Pilot Training (BE, SB, CG, JG), pp. 682–687.
- EDM-2016-FeildLZRE #automation #feedback #framework #learning #platform #scalability
- A Scalable Learning Analytics Platform for Automated Writing Feedback (JLF, NL, NLZ, MR, AE), pp. 688–693.
- EDM-2016-RawatA #automation #feedback
- An Automated Test of Motor Skills for Job Selection and Feedback (BPS, VA), pp. 694–699.
- EDM-2016-Mojarad #performance #student #using
- Studying Assignment Size and Student Performance Using Propensity Score Matching (SM), pp. 701–702.
- EDM-2016-SabourinKHM #automation #feedback #student #towards
- Toward Automated Support for Teacher-Facilitated Formative Feedback on Student Writing (JS, LK, KH, SWM), pp. 703–704.
- EDM-2016-YadavSKSD #framework #learning #named #platform
- TutorSpace: Content-centric Platform for Enabling Blended Learning in Developing Countries (KY, KS, RK, SS, OD), pp. 705–706.