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AIED
2007
Springer

177views Artificial Intelligence» more AIED 2007»

Evaluating a Collaborative Constraint-based Tutor for UML Class Diagrams

13 years 8 months ago

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COLLECT-UML is a collaborative constraint-based tutor for teaching object-oriented analysis and design using Unified Modelling Language. It is the first system in the family of con...

Nilufar Baghaei, Antonija Mitrovic

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Id	Views	Title	Status

1	177	Evaluating a Collaborative Constraint-based Tutor for UML Class Diagrams	claim
2	166	Who Says Three's a Crowd? Using a Cognitive Tutor to Support Peer Tutoring	claim
3	151	Choosing Reading Passages for Vocabulary Learning by Topic to Increase Intrinsic Motivation	claim
4	150	Can Help Seeking Be Tutored? Searching for the Secret Sauce of Metacognitive Tutoring	claim
5	142	Assessing Learning in a Peer-Driven Tutoring System	claim
6	140	Mind and Body: Dialogue and Posture for Affect Detection in Learning Environments	claim
7	138	MathGirls: Toward Developing Girls' Positive Attitude and Self-Efficacy through Pedagogical Agents	claim
8	136	Towards promoting meta-cognition using emotive interface personas within Open-Learner Modelling Environments	claim
9	130	Affect and Usage Choices in Simulation Problem-Solving Environments	claim
10	126	Dialog Convergence and Learning	claim
11	124	What Level of Tutor Interaction is Best?	claim
12	122	Novel Tools for Assessing Student Discussions: Modeling threads and participant roles using speech act and course topic analysis	claim
13	121	Learning Tutorial Rules Using Classification Based On Associations	claim
14	121	'Tis Better to Construct than to Receive? The Effects of Diagram Tools on Causal Reasoning	claim
15	120	Situation Creator: A Pedagogical Agent Creating Learning Opportunities	claim
16	117	A Spoken Translation Game for Second Language Learning	claim
17	116	Profiling Student Interactions in Threaded Discussions with Speech Act Classifiers	claim
18	116	Investigating Generative Factors of Score Matrices	claim
19	113	Modeling learning patterns of students with a tutoring system using Hidden Markov Models	claim
20	110	Domain-Specific and Domain-Independent Interactive Behaviors in Andes	claim
21	108	Beyond the code-and-count analysis of tutoring dialogues	claim
22	108	Porting an Intelligent Tutoring System across Domains	claim
23	107	Measuring the Effect of Collaboration in an Assessment Environment	claim
24	106	Benefits of Handwritten Input for Students Learning Algebra Equation Solving	claim
25	106	Is Over Practice Necessary? - Improving Learning Efficiency with the Cognitive Tutor through Educational Data Mining	claim
26	105	Emotions and Learning with AutoTutor	claim
27	104	Helping Courseware Authors to Build Ontologies: The Case of TM4L	claim
28	103	Predicting Students' Performance with SimStudent: Learning Cognitive Skills from Observation	claim
29	102	Relating Machine Estimates of Students' Learning Goals to Learning Outcomes: A DBN Approach	claim
30	102	Can a Polite Intelligent Tutoring System Lead to Improved Learning Outside of the Lab?	claim
31	102	Empowering researchers to detect interaction patterns in e-collaboration	claim
32	100	Pedagogical Agents Trying on a Caring Mentor Role	claim
33	99	Comparing Student-Constructed Open Learner Model Presentations to the Domain	claim
34	99	Intelligent Tutoring and Human Tutoring in Small Groups: An Empirical Comparison	claim
35	99	Optimizing Student Models for Causality	claim
36	98	Comparing Linguistic Features for Modeling Learning in Computer Tutoring	claim
37	97	Tools for Authoring a Dialogue Agent that Participates in Learning Studies	claim
38	96	Changing Attitudes and Performance with Computer-generated Social Models	claim
39	96	Supporting Collaborative Idea Generation: A Closer Look Using Statistical Process Analysis Techniques	claim
40	95	Accelerated Future Learning via Explicit Instruction of a Problem Solving Strategy	claim
41	94	The Effect of Problem Templates on Learning in Intelligent Tutoring Systems	claim
42	93	Effect of Metacognitive Support on Student Behaviors in Learning by Teaching Environments	claim
43	88	Fitting Spatial Ability into Intelligent Tutoring Systems Development	claim
44	88	Getting Under the Skin of Learners: Tools for Evaluating Emotional Experience	claim
45	87	Sun Seeking: Interactive Story-Reading Through Different Media	claim
46	87	Does Learner Control Affect Learning?	claim
47	87	Using Machine Learning Techniques to Analyze and Support Mediation of Student E-Discussions	claim
48	87	Taking advantage of the Semantics of a Lesson Graph based on Learning Objects	claim
49	86	Applying Learning Factors Analysis to Build Stereotypic Student Models	claim
50	85	Constraint Authoring System: An Empirical Evaluation	claim
51	84	Tutorial Dialogue as Adaptive Collaborative Learning Support	claim
52	84	How did the e-learning session go? The Student Inspector	claim
53	83	Open Learner Models: Opinions of School Education Professionals	claim
54	80	Standard-Compliant Scenario Building with Theoretical Justification in a Theory-Aware Authoring Tool	claim
55	79	AIED Applications in Ill-Defined Domains	claim
56	79	The Influence of Learner Characteristics on Task-Oriented Tutorial Dialogue	claim
57	78	Lowering the Bar for Creating Model-Tracing Intelligent Tutoring Systems	claim
58	78	Using Ontologies for an Effective Design of Collaborative Learning Activities	claim
59	78	Motivationally Intelligent Systems: Diagnosis and Feedback	claim
60	76	Linking Educational Materials to Encyclopedic Knowledge	claim
61	75	VL-PATSy: Facilitating vicarious learning via intelligent resource provision	claim
62	74	Understanding Student Answers In Introductory Physics Courses	claim
63	73	Combining Bayesian Networks and Formal Reasoning for Semantic Classification of Student Utterances	claim
64	73	Student Motivation and Performance in Scientific Problem Solving Simulations	claim
65	69	Context Based Classification for Automatic Collaborative Learning Process Analysis	claim
66	67	Learning Engagement: What Actions of Learners Could Best Predict It?	claim
67	66	Topic Initiative in a Simulated Peer Dialogue Agent	claim
68	55	Explaining Self-Explaining: A Contrast between Content and Generation	claim

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