TABLE 10.1(Continued)
Alexander,Kulikowich, &
Schulze (1994)
Alexander, Jetton,
& Kulikowich
(1995)
Alexander, Murphy,Woods, Duhon, &
Parker (1997)
Alexander &
Murphy (1998)
Murphy &
Alexander (2002)
Alexander, Sperl,Buehl, Fives, &
Chiu (2002)
Analyses
MANOVA; Re-
gression
MANOVA; Cluster
analysis
MANOVA; SEM
Cluster analysis
MANOVA;
Path
analyses
MANOVA; Cluster
analysis
Findings
Domain knowledge
predicted stu-dents’ recall andinterest; Rela-tions betweendomain knowl-edge, interest,and recall grewstronger acrossperformancegroups
Exp. 1: Three dis-
tinct clustersvarying byknowledge, inter-est, and recall
Exp. 2: Four clus-
ters ranging fromhigh knowledge,interest, and re-call to low on allvariables
Significant in-
creases in knowl-edge and interestover time anddecreased use oftext-based (sur-face-level) strate-gies; expected re-lations betweenand amongmodel factors
Three distinct clus-
ters formed atpretest and fourclusters wereidentified atposttest. Charac-teristics of clus-ters confirmedstrongly tomodel predic-tions.
Path analyses
showed thatposttest subject-matter knowl-edge was directlyand indirectlypredicted by pre-test knowledge,surface- anddeep-level strate-gies, interactiveknowledge, andpretest interest
Four clusters
emerged with thecharacteristics ofacclimation, mid-competence,high-competence,and proficiency.
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