### Table II. Comparison of AEHS that use learning style as a source of adaptation System Domain Learning Style Model Adaptation based on Learning Style Diagnosis Approach amp; Dynamic Adaptation

2004

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### Table 4. Factorial array for model selection with dynamic trajectory learning: value of AIC/BIC based on the number of trajectories (T) and the number of hidden neurons (H)

2002

"... In PAGE 4: ... 4.2 Three Classes: Ribosomal, Transcription and Secretion Gene Functional Classes Table4 provides computed statistical criteria for model selection: values of AIC/BIC with average of five independent runs based... ..."

Cited by 1

### Table 5.1 and Table 5.2, the learned actions do significantly outperform a painstakingly developed hand-coded policy. Moreover, the learned controllers were able to achieve greater functionality. On the test track with curves, the hand-coded controller was not able to navigate the entire route at BGBHD1BPD7 without driving off the road. The learned controller was able to make it through the entire track. Longitudinal control, in particular, is much more challenging when using the complex dynamics. Just maintaining the current speed can be difficult, since the road, aerodynamic, and engine forces act against the vehicle and having no throttle will result in the vehicle slowing down. The same structure was used for models as in the simple dynamics. In the case of the complex dynamics, it was not possible to learn the model exactly since it no longer exactly fit the true system, but using the mean of the various state variables from the model was still quite effective.

2002

### Table 1 gives an overview of model parameters used in the network model. The table indicates that the basic network dynamics of neurons and couplings is speci ed by two parameters, the learning rate a indicating the velocity of coupling changes and the uctu- ation rate T of neuronal uctuations. It is realistic to assume limited resources at neurons

"... In PAGE 12: ... As a result of that analysis one obtains the resulting emerging percepts and their changes due to the slow resource de cit dynamics and due to statistical uctuations. Table1 : Model parameter used in the network model. Notat.... ..."

### Table 1. Effector Dynamics and Limits.

### Table 1 Survey of mobile learning systems

"... In PAGE 1: ... The prototype implementation uses XML, XSL transformation, Document Object Model (DOM) and Active Server Pages (ASP) for dynamic interaction. 2 Review of Mobile Learning System Implementation Table1 depicts a summary of relevant mobile learning systems implementations that were discussed during the recent IEEE workshop for Wireless And Mobile Technologies in Education (WMTE 2002, Sweden). A few observations can be made: (a) Mobile learning is in its infancy stage.... ..."

### Table 3. Results of Hierarchical Regression Models of Student Learning1 ___________________________________________________________ Student Learning ________________________ Variables Model 1 Model 2 ___________________________________________________________ Gender 0.39+ 0.27

2002

"... In PAGE 10: ...f the variables measured using the survey (mean=5.37, S.D.=1.18 on a seven-point scale) which suggests that students in the study generally had a fairly high level of perceived learning. Table3 presents the results of a hierarchical regression analysis of these variables. The first-step regression model revealed moder- ate relationships between age and perceived learning and gender and perceived learning, suggesting that older students and women may have had stronger learning experiences in the online envi- Business Communication Quarterly 63:4 December 2000 __________________________________________________________________________... In PAGE 11: ...Table3 ). However, these effects became non-signifi- cant in the full model.... In PAGE 11: ...cant in the full model. In the full model, the only variables that are significantly associated with learning are the three variables for interaction: instructor emphasis on interaction, ease of interac- tion, and classroom dynamics (see Table3 ). Considering that the learner-interface dimension of interaction remained constant since all courses used the same course software, these results sug- gest that instructors play a significant role in enhancing learning in Internet-based courses through their efforts to generate and facilitate interaction.... ..."

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### Table 5. Learning techniques

2003

"... In PAGE 22: ...nderlying technologies, e.g., support vector machines [Cristianini and Shawe-Taylor, 2000]; improved links between reinforcement learning and stochastic control theory [Bertsekas and Tsitsiklis, 1996]; advances in planning and learning methods for stochastic environments [Littamn, 1996; Parr, 1998]; and improved theoretical models of simple genetic algorithms [Vose, 1999]. Major types of learning techniques are summarized in Table5 [Zimmerman and Kambhampati, 2001; Nordlander, 2001]. ... ..."

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### Table 3. Dynamic models

"... In PAGE 4: ...0000) (1.0000) Table3 . The in nominal signi cant.... In PAGE 6: ... We then proceeded to the estimation of the dynamic model taking a general-to-speci c approach. The parsimonious model reported in Table3 has dynamic terms in nominal wages, in ation and productivity, and it passes several diagnostic tests. There is no evidence of structural 668 G.... In PAGE 6: ... 6. France, C S MSQ test an ECM and dynamic terms in prices and unemployment (see Table3 ). Serial correlation, normality and hetero- scedasticity tests are all passed, and so are predictive failure and Chow tests.... ..."

### Table 3. Parameter Estimates of Basic Rule Learning Model.

in Aspiration-based and Reciprocity-based Rules in Learning Dynamics for Symmetric Normal-Form Games

"... In PAGE 16: ... We pool over games in order to try to identify regular features of learning dynamics that are general and not game-specific, so we can be more confident that these features will be important in predicting out-of-sample behavior. The ML parameter estimates are given in Table3 , and the maximized LL is -8550.21.... ..."