Angel Jannasch-Pennell

Using SAS/IntrNet for Evaluating Web-Based Courses.

Abstract This article describes the use of SAS/IntrNet to evaluate and enhance university level web‐based courses. Since the release of SAS/IntrNet in October 1997, a number of major corporations and government agencies such as Ford Credit, National Semiconductor, and Danish Institute of Agricultural Sciences have implemented SAS/IntrNet for their intranet and internet servers (SAS Institute, 1997). IntrNet provides the ability to create both static and dynamic Web pages. The product uses web‐publishing tools to convert SAS output to hypertext markup language (html) for the creation of static Web pages.

Teaching Factor Analysis in Terms of Variable Space and Subject Space Using Multimedia Visualization

There are many common misconceptions regarding factor analysis. For example, students do not know that vectors representing latent factors rotate in subject space, rather than in variable space. Consequently, eigenvectors are misunderstood as regression lines, and data points representing variables are misperceived as data points depicting observations. The topic of subject space is omitted by many statistics textbooks, and indeed it is a very difficult concept to illustrate. An animated tutorial was developed in an attempt to alleviate this problem. Since the target audience is intermediate statistics students who are familiar with regression, regression in variable space is used as an analogy to lead learners into factor analysis in the subject space. At the end we apply the Gabriel biplot to combine the two spaces. Findings from a textbook review, a survey, and a “think aloud” protocol were taken into account during the program development and are discussed here.

Enhancement of Student Experience Management in Higher Education by Sentiment Analysis and Text Mining

Theobjectiveofthiscasestudyistoillustratehowtextminingofopen-endedresponsesandsentimental expressions(positiveornegative)fromstudentsurveycouldyieldvaluableinformationforimproving studentexperiencemanagement(SEM).TheconceptofstudentSEMwasborrowedfromthenotionof customerexperiencemanagement(CEM),whichaimsforongoingimprovementofcustomerrelations throughunderstandingofthecustomer’spointofview.Withtheadvanceoftextminingtechnology, whichisbaseduponartificialintelligenceandmachinelearning,textualdatathatwerepreviously underutilizedarefoundtobevaluableinCEM.ToillustratehowtextminingcanbeappliedtoSEM, theauthorsdiscussanexamplefromacampus-widesurveyconductedatArizonaStateUniversity.The purposeofthissurveywastobetterunderstandstudentexperienceswithinstructionaltechnologyin orderforadministratorstomakedata-drivendecisionsonitsimplementation.Ratherthanimposing the researchers’ preconceived suppositions on the students by using force-option survey items, researchersonthisprojectchosetouseopen-endedquestionsinordertoelicitafreeemergenceof themesfromthestudents.Themostvaluablelessonlearnedfromthisstudyisthatstudentsperceive anidealenvironmentasawebofmutuallysupportingsystems.Specifically,onlineaccessshouldbe augmentedbyuseoflaptopsandavailabilityofcoursematerials,whereasvirtualclassesshouldbe balancedbyhumaninteractions.