Tiffany Barnes, Michel C. Desmarais, Cristóbal Romero, Sebastián Ventura
Proceedings of the Second International Conference on Educational Data Mining
EDM, 2009.

@proceedings{EDM-2009,
	editor        = "Tiffany Barnes and Michel C. Desmarais and Cristóbal Romero and Sebastián Ventura",
	isbn          = "978-84-613-2308-1",
	publisher     = "{www.educationaldatamining.org}",
	title         = "{Proceedings of the Second International Conference on Educational Data Mining}",
	year          = 2009,
}

Contents (33 items)

EDM-2009-AyersND #comparison #student

A Comparison of Student Skill Knowledge Estimates (EA, RN, ND), pp. 1–10.

EDM-2009-Baker #difference

Differences Between Intelligent Tutor Lessons, and the Choice to Go Off-Task (RB), pp. 11–20.

EDM-2009-Ben-NaimBM #adaptation #approach #data-driven #education

A User-Driven and Data-Driven Approach for Supporting Teachers in Reflection and Adaptation of Adaptive Tutorials (DBN, MB, NM), pp. 21–30.

EDM-2009-AgapitoO #detection #performance #using

Detecting Symptoms of Low Performance Using Production Rules (JBA, AO), pp. 31–40.

EDM-2009-DekkerPV #case study #predict #student

Predicting Students Drop Out: A Case Study (GD, MP, JV), pp. 41–50.

EDM-2009-FengBH #composition #education #learning #using

Using Learning Decomposition and Bootstrapping with Randomization to Compare the Impact of Different Educational Interventions on Learning (MF, JB, NTH), pp. 51–60.

EDM-2009-GongRBH #learning #question #self #student

Does Self-Discipline impact students’ knowledge and learning? (YG, DR, JB, NTH), pp. 61–70.

EDM-2009-HershkovitzN #consistency #learning #online #student

Consistency of Students’ Pace in Online Learning (AH, RN), pp. 71–80.

EDM-2009-MadhyasthaT #assessment #consistency #feedback #online #student

Student Consistency and Implications for Feedback in Online Assessment Systems (TMM, SLT), pp. 81–90.

EDM-2009-NagataTSKM #recommendation

Edu-mining for Book Recommendation for Pupils (RN, KT, KS, JK, KM), pp. 91–100.

EDM-2009-NugentAD #clustering #identification #student

Subspace Clustering of Skill Mastery: Identifying Skills that Separate Students (RN, EA, ND), pp. 101–110.

EDM-2009-PardosH #order #problem #random #set

Determining the Significance of Item Order In Randomized Problem Sets (ZAP, NTH), pp. 111–120.

EDM-2009-PavlikCK #analysis #automation #domain model #learning #modelling #using

Learning Factors Transfer Analysis: Using Learning Curve Analysis to Automatically Generate Domain Models (PIPJ, HC, KRK), pp. 121–130.

EDM-2009-PrataBCRC #collaboration #comprehension #detection #learning

Detecting and Understanding the Impact of Cognitive and Interpersonal Conflict in Computer Supported Collaborative Learning Environments (DP, RSJdB, EC, CPR, YC), pp. 131–140.

EDM-2009-RaiGB #parametricity #using

Using Dirichlet priors to improve model parameter plausibility (DR, YG, JB), pp. 141–150.

EDM-2009-RitterHNDMT

Reducing the Knowledge Tracing Space (SR, TKH, TN, DD, RCM, BT), pp. 151–160.

EDM-2009-RusLA #automation #detection #information management #modelling #student

Automatic Detection of Student Mental Models During Prior Knowledge Activation in MetaTutor (VR, MCL, RA), pp. 161–170.

EDM-2009-SimkoB #adaptation #automation #concept

Automatic Concept Relationships Discovery for an Adaptive E-course (MS, MB), pp. 171–180.

EDM-2009-StamperB #metric

An unsupervised, frequency-based metric for selecting hints in an MDP-based tutor (JCS, TB), pp. 181–190.

EDM-2009-SacinASO #data mining #education #mining #recommendation #using

Recommendation in Higher Education Using Data Mining Techniques (CVS, JBA, LS, AO), pp. 191–199.

EDM-2009-AbbasS #learning #using

an Argument Learning Environment Using Agent-Based ITS (ALES) (SA, HS), pp. 200–209.

EDM-2009-AnayaB #approach #collaboration #data mining #framework #mining

A Data Mining Approach to Reveal Representative Collaboration Indicators in Open Collaboration Frameworks (ARA, JB), pp. 210–219.

EDM-2009-Barker-PlummerCD #first-order #logic #natural language

Dimensions of Difficulty in Translating Natural Language into First-Order Logic (DBP, RC, RD), pp. 220–229.

EDM-2009-CetintasSXH #correctness #low level #predict #problem

Predicting Correctness of Problem Solving from Low-level Log Data in Intelligent Tutoring Systems (SC, LS, YPX, CH), pp. 230–239.

EDM-2009-FengB #modelling

Back to the future: a non-automated method for constructing transfer models (MF, JB), pp. 240–249.

EDM-2009-Hardof-JaffeHABN #how #question #student

How do Students Organize Personal Information Spaces? (SHJ, AH, HAK, OB, RN), pp. 250–258.

EDM-2009-HeinerZ #classification #student

Improving Student Question Classification (CH, JLZ), pp. 259–268.

EDM-2009-MostowB #how #lessons learnt #what #why

Why, What, and How to Log? Lessons from LISTEN (JM, JB), pp. 269–278.

EDM-2009-PechenizkiyTVAB #assessment #mining #online #process

Process Mining Online Assessment Data (MP, NT, EV, WMPvdA, PDB), pp. 279–288.

EDM-2009-QuevedoM #assessment #education #learning #process

Obtaining weights of a rubric through a pairwise learning model when the assessment process involves more than one lecturer (JRQ, EM), pp. 289–298.

EDM-2009-RomeroVGCG #collaboration #data mining #education #mining

Collaborative Data Mining Tool for Education (CR, SV, EG, CdC, MG), pp. 299–308.

EDM-2009-ZafraV #learning #multi #predict #programming #search-based #student

Predicting Student Grades in Learning Management Systems with Multiple Instance Learning Genetic Programming (AZ, SV), pp. 309–318.

EDM-2009-ZoubekB #difference #visualisation

Visualization of Differences in Data Measuring Mathematical Skills (LZ, MB), pp. 319–324.