The influence of narrative and expository lesson text structures on knowledge structures: alternate measures of knowledge structure     

Roy?B.?ClarianaEmail author  Michael?B.?Wolfe  Kyung?Kim 《Educational technology research and development : ETR & D》2014,62(5):601-616

This investigation applies two approaches for representing and comparing text structures as undirected network graphs to describe the influence of narrative and expository lesson texts on readers’ knowledge structure elicited as free recall. Narrative and expository lesson texts and undergraduate participants’ free recall essays (n = 90) from a study by Wolfe and Mienko (Br J Educ Psychol 77, 541–564, 2007) were reanalyzed for lexical proximity as sequential occurrence of selected important terms in the text and as actual minimum distances between these terms. The proximity data were then rendered as Pathfinder networks for analysis. Compared to human-rater benchmark measures, the convergent validity of the sequential approach (range of r = .53 to .83, median r = .70) was a little better than that of the minimum distance approach (.51 to .80, median r = .67). Further, we anticipated that the lesson text structure would be reflected in the text structure of the free recall essays, but this was not observed. On average, the essays in all three lesson conditions tended to converge on a sequential expository structure. Further, compared to the expository lesson texts, the narrative lesson text had a distinctly different influence on posttest recall essay text structures. Overall then, the sequential occurrence approach appears to provide a reasonably good, automatically derived method for representing and comparing lesson texts and participants’ essays as network graphs. If further confirmed and fully automated, there is a wide range of application of such measurement approaches for learning and research.  相似文献   

Decentralized low-complexity tracking of uncertain interconnected high-order nonlinear systems with unknown high powers     

Sung Jin Yoo  Tae-Hyoung Kim 《Journal of The Franklin Institute》2018,355(11):4515-4532

A global decentralized low-complexity tracker design methodology is proposed for uncertain interconnected high-order nonlinear systems with unknown high powers. It is assumed that interconnected nonlinearities are bounded by completely unknown nonlinearities, rather than, a linear combination of high-ordered state variables. Compared with the existing decentralized results for interconnected nonlinear systems with known high powers, the decentralized robust controller, which achieves the pre-designable transient and steady-state tracking performance for each subsystem, is designed by employing nonlinear error surfaces with time-varying performance functions, regardless of unknown nonlinear interactions and high powers related to virtual and actual control variables. The proposed decentralized continuous robust low-complexity tracker is realized without the use of any adaptive or function approximation techniques for estimating unknown parameters and nonlinearities. The stability and preassigned tracking performance of the resulting decentralized low-complexity control system are thoroughly analyzed in the Lyapunov sense. Finally, simulation results on coupled underactuated mechanical systems are provided to show the effectiveness of the proposed theoretical result.  相似文献