A Multivariate Survey Analysis: Evaluation of Technology Integration in Teaching Statistics

Transcription

1 A Multivariate Survey Aalysis: Evaluatio of Tehology Itegratio i Teahig Statistis Abdellatif Thathae ad Paulie Carolye Fortes Teahig ad learig i higher eduatio has bee iflueed by the rapid rate of iovatio i tehology. We have experimeted with the itegratio of tehology i our foudatio Statistis subjet ad measured studets performae relative to those taught statistis by the traditioal teahig of the same subjet: a total of 144 studets of 30 differet atioalities taught by the ew methodology were surveyed at the ed of the subjet before the fial examiatio. Keywords: itegratio of tehology, teahig statistis, multivariate aalysis, pedagogy Itrodutio Statistis played a vital role i our daily math world. It is a method used o how to iterpret, aalyze ad evaluate the fidigs of ay researh iquiries. Havig this i mid, a irease of dilemma o how to ope with the shiftig from traditioal tool to moderize method. A growig movemet has see i itroduig statistis i all levels of eduatio (Garfield & Ahlgre, 1988). May statistiias as well as mathematis teahers have bee ivolved i this reform. I the moder soiety, there is a strog awareess with the relevae of statistis i ay form of researh ad fat fidig artiles. Thus, uiversity studets ad teahers still fid Mathematis ad Statistis a axiety-provokig ad diffiult subjet. True eough that may researh studies ofirmed this pereptio (Baharu & Porter, 2009; Fortes & Thathae, 2010). Aother hallege that alled the attetio of may statisti eduators is the diverse group of studets with differet soietal traits, expetatios ad bakgrouds makig the teahig methodology more diverse (Peiris & Beh, 2006; Fortes & Thathae, 2010). Therefore, it is importat for teahers to put a stadard method o how to teah statisti effetively with the use of either Sietifi Calulator (traditioal tool) or Exel Spread sheet (moderized tool or tehology). Itegratio of tehology is really pereived by eduators as a very importat tool for effetive delivery of teahig for all levels of eduatio. This oept has bee supported by Natioal Couil for Mathematis Teahers ad Ameria Statistial Assoiatio. I U.A.E., very seldom we see artiles ivolvig ew Proeedigs asilite 2011 Hobart: Full Paper 1227

2 tehology as a moderize tool i teahig Statistis as ompared to the umerous studies oduted i Australia, USA, ad New Zealad. Explorig the impat of teahig statistis with the use of Exel as a ew tehology has bee a hallege ot oly to eduators but to studets as well. The Uiversity of Wollogog i Dubai, teahes itrodutio to statistis with the use of traditioal method whih is usig oly a Sietifi Calulator. I the reet years, studets were required to take dow otes durig lass leture or tutorial ad solvig problems with the aid of a sietifi alulator; moreover graphi alulators were ot allowed durig examiatio. Curretly, the Uiversity is usig tehologial tools suh as olie ourse materials wherei we provide up dated leture ad tutorial materials whih a be foud by our studets i the uiversity website, but still we fid this approah iadequate for studets to lear statisti i a more simplified way. It is well-thought limited beause studets are ot able to explore real, large ad omplex data ad studet are ofied with formulas, sometimes they fid it vague or abstrat. More ofte tha ot, studets fid Statistis more diffiult ad ompliated ompared to Mathematis subjets. Diffiulty i uderstadig Statisti beame a major oer to all eduators. This has drive them to a questio o how to improve the aademi performae of the studets ad motivate them i uderstadig the oepts with the use of a ew method to simplifyig the operatios of statistis. To address these issues, we attempted to itegrate the ew tehology as a ew tool i teahig statistis that will ot oly remai but will improve the urriulum ad without sarifiig the otet of the subjet. Though we expet halleges ahead with this iovatio ad pereive that through the use of this ew tehology it will further failitate studets to lear statistial oepts with greater uderstadig ad ease. It will retify studets from the burde of workig out o the statistial formulas from whih they a have more time to aalyze, iterpret simple or omplex data, ad justify their olusios based o a data. Studets a develop positive attitudes i lasses that ilude omputer-based istrutio ad ollaborative group work. We therefore, aim to aswer the followig researh questios: i. I teahig itrodutio to statisti, will the itegratio of a ew tehology have a effet o studet s aademi performae? ii. What will be the pereptio of the studets i the itegratio of a ew tehology that will be used i statisti iii. Are the learig outomes suh as orgaizig data ito tables ad graphs, summarize data usig appropriate statistial methods, able to draw olusio from the data, ad show relevae of statistis to a wide rage of disiplie i everyday life is ahieve usig tehology This study will attempts to fill the gap of literature from differet outries for it a be omparable fator with UAE sie the fous of teahig statistis is ot yet as popular as teahig mathematis here i the regio. Tehology i Teahig Statistis I the advet of tehology ad its gaiig popularity i the 21 st etury, there was a eed to itegrate tehology i teahig ad learig i the aademi subjet areas (Neiss, 2005). This reform radially affets what we teah ad alter our way of teahig. Similarly, i 2005, Thomas ad Hog (ited i Neiss, 2005) developed the oept of teahers suh pedagogial tehology kowledge (PTK), ad reetly kow as (TPCK). From this oept, tehology has bee a importat istrumet for learig statistis, hee teahers must also develop a overarhig oeptio of their subjet matter i teahig with tehology (Neis, 2005). Itegratio of tehology i teahig ad learig is about the otet ad effetive pedagogy. I the ase of Statistis, the substatial hage i teahig statistis reated strog syergies betwee tehology, pedagogy, Proeedigs asilite 2011 Hobart: Full Paper 1228

3 ad otet (Moore, 1997; Vellema, 1995). Aordig to Moore (1997) requirig studets to work i groups ad disussig their works orally ad i writig, various diagosti tools to aalyze data, ad omputeritesive statistial prative failitates studet learig. Figure 1. illustrates the framework of tehology, pedagogy, otet ad kowledge. Figure 1. The Ve diagram of TPACK I additio, Natioal Couil of the Teahers i Mathematis has this tehology priiple Tehology is essetial i teahig ad learig; it ifluees the mathematis that is taught ad ehaes learig (NCTM, 2000). It further explaied that tehology suh as alulators ad omputers are reshapig the mathematial ladsape ad eourage shool mathematis to reflet the hages. I this priiple, with the use of tehology appropriately ad osietiously, studets a lear mathematis more deeply, speulate ad make iferees ad be able to work at higher levels of geeralizatio or abstratio (NCTM, 2000). These priiples suggest therefore that tehology plays a very importat role i the learig urve of the studets. Similarly, the Ameria Statistial Assoiatio (ASA) supported the priiple the use of tehology for developig oeptual uderstadig ad aalyzig real data (GAISE, 2007). Various ivestigatios has bee made o the differet approahes of teahig methodology with the itegratio of tehology ad the impat i studet s learig (Neiss, 2005; Baharu & Porter, 2009; Gorma, 2008; Sam & Kee, 2004; Prabhakar, 2008; Tsao, 2006). May govermet ageies eve ivested huge amout of moey i professioal developmet of mathematis teahers i tehology-ehaed teahig ad learig. I Puerto Rio, the Istitute for the Ehaemet of Teahig ad Learig (IDEAS) was reated i Oe ompoet of the istitute is the Faulty of Developmet Program foused o otet ad pedagogial tehiques through the use of tehology i the lassroom, o-traditioal teahig ad learig styles i ehaig teahig ad learig to help studets to lear (Morales & Roig, 2002). Iterestigly, the most importat impat of the itegratio of tehology i the lassroom was the studets ejoyed the learig experiees ad resulted to a more resposible for their ow learig. Though there may be drawbak of tehology-ehaed teahig ad learig, still outweighed by the advatages based o the previous researh fidigs. Here are some of the positive effets of itegratio to the ew tehology based o the researh fidigs: itegratio of tehology a be used as a tool for supportig ad ehaig studet s learig wider learig beefits whih may arue from itegratig ICT a provide uique opportuities for studets to do mathematial tasks i ew ways that may have Proeedigs asilite 2011 Hobart: Full Paper 1229

4 foster learig ad developmet sigifiat tool for promotig mathematial problem solvig, reasoig, ad exploratio studets are motivated to geeralized ad formalize so that they a devie their ow ways to ommad the omputer to draw graphs or to solve umerial problems studets a build their ow uderstadig usig omputers as resoure tools, or as a ommuiatio tool to share their ideas with other learers They a share ad ompare their idividual uderstadig ad experiees. Tehologial Tools i Statistis Istrutio The rapid popularity of the apabilities of tehology ireased, ad more software tools have bee released, ad tehology has bee osidered i failitatig studets learig of statistis (Garfield, Chae & Sell, 2000). While software has bee available for doig statistial aalysis, the use of tehology i teahig ad learig statistis is otiuously developig. There are several types of tehology beig use i statistis istrutios amely, statistial pakages ad spreadsheets, Web or omputer-based tools, graphi alulator, or programmig laguages. Calulators ad omputers redue the omputatioal burde; allow more extesive exploratio of statistial oepts. I Malaysia, i 2000, there was all of for a eed to itegrate iformatio tehology i the teahig ad learig of mathematis (Sam & Kee, 2004). Though some of the teahers as well as parets believed that the use of alulators may lose basi mathematial skills ad uderstadig of the studets as the prerequisite for advae mathematis, teahers itrodued graphi alulator i teahig Math ourses. The result idiated a positive impat o the ulture of statistial learig, studets beame ative i group partiipatio, ad studets ejoyed learig statistis ad also improved their uderstadig ad skill i statistis. Aordig to Liag (2000) omputer programs showed studets attrated to the iterative omputer programs desiged for busiess statistis ourse, studets were motivated to atted lasses whe omputer programs are applied to teahig. I additio, studets were able to uderstad ofusig topis, ad felt that teahig them to use omputer failities really improves their ow abilities to apply similar programs i aalyzig real-world problems. As metioed i Sharma & Barrett (2007), supportig a ourse with tehology a allow learers ad teahers more flexibility i both time ad plae, ad omplemets ad ehaes fae-to-fae teahig. Itroduig ew tehology ito the lassroom a preset halleges with studets reeptio aeptae (Gorma, 2008) ad the use of tehology for teahig statistis has bee explored reetly (Su & Liag, 2000; Morales & Roig, 2002; Baharu & Porter, 2009; Prabhakar, 2008; Vellema, 1995,) ad fidigs suggested more o positive impat. Nowadays, studets are more ofidet with omputers, this a support to motivate studets, ad apparetly usig omputer appliatios is more effetive learig (Peiris & Beh, 2006). It is lear that the eed to stregthe researh ito statistis eduatio is beomig more ad more relevat i may workfores whot are ivolved i deisio-makig proess (Peiris & Beh, 2006; Peiris & Peseta, 2004).Therefore, this study is based o the oept that lassroom teahig bleded with tehology a be a sigifiat fator i promotig aademi iovatio ad i trasformig the teahig ad learig i statistis paradigm. Teahig Methodology ad Survey Desig The typial otet of itrodutio to statistis is divided ito three setios: desriptive statistis, probability theory ad iferetial statistis. Desriptive statistis iludes presetatio of data (harts, frequey distributio table, histogram, polygo, satter plot, ad box-plot), measures of etral tedey (mea, media, ad mode), ad measures of dispersio (rage, iterquartile, variae, oeffiiet of variatio, stadard Proeedigs asilite 2011 Hobart: Full Paper 1230

5 deviatio). Probability theory overs rules of additio ad multipliatio, idepedet ad mutually exlusive evets, margial ad joit probability, probability distributios, ormal distributios, ad iferetial statistis iludes samplig ad estimatig populatio mea ad ofidee iterval. This ew subjet desiged iludes 1-hour leture, 1-hour tutorial ad 1-hour omputer laboratory. We itrodued MS Exel as part of the pedagogy whih is a widely used pakage, user friedly, aessible ad ost-effetive. Studets lear to set up a simple spreadsheet ad use it i posig ad solvig problems, examiig data, ad ivestigatig patters/distributio of the data, produig summary statistis ad harts, writig equatios ad usig data aalysis tools ad Exel statistial ommads. Studets are expeted to atted eah leture but ot ompulsory, while attedae is ompulsory for eah tutorial & omputer laboratory lass withi a 13-week of sessio. All leture otes, tutorial ad laboratory works, review materials for all assessmets were uploaded i the uiversity itraet. For the survey, a strutured questioaire was used to ollet the demographi iformatio about the studets whih also iludes geder, atioality, ad bakgroud i statistis (if there s ay). There were questios about their pereptio towards Statistis, the teahig styles, expetatios ad their pereptio o the itegratio i the ourse. These items were measured usig a 5-poit Liker type sale, ragig from Strogly Disagree (SD) to Strogly Agree (SA). A statistial aalysis was arried out to test the hypotheses of this researh. Fidigs Survey Aalysis There were 144 studets resposes out of 162 offiially erolled i the itrodutory Statistis offered by the Faulty of Computer Siee ad Egieerig at Uiversity of Wollogog i Dubai. Demographi iformatio suh as atioality, geder, expeted grades i Mathematis ad Statistis, as well as the pereptio of studets towards teahig with the use of tehology data were olleted through strutured questioaire. Out of 144 respodets, 47.22% were female ad 52.8% were male from 30 differet atioalities. About 60.4% (87 out of 144) are first time to study Statistis while 39.6% (57 out of 144) already studied i their seodary eduatio or repeaters. Survey details showig the peretages of their resposes i eah item with the orrespodig mea ad stadard deviatio are give i Appedix A. The survey results idiate the followig: 1- more tha 86% of the studets feel kowledgeable i orgaizig data i tables, produig graphs ad summarizig data. 2- oly 18% of the studets feel that statistis is harder tha Mathematis 3-35% of the studets feel that the 2 hours lab is too log while 47% are happy with that. 4-53% amog the good studets (with C or D or HD) thik that a oe hour leture is ot eough. 4-80% of the studets have a positive pereptio towards the teahig staff 5-40% amog the weak studets with (F or PC or P) reommed the book ad foud it helpful versus oly 30% amog the good studets (C or D or HD) 6-56% amog the weak studets fid probability the hardest topi i Statistis 8- Amog those who have already take statistis 75.5% got above average ompared to about 71% amog those who have take statistis for their first time. Iferee about the differee betwee the traditioal ad tehology teahig I order to measure whether studets performae whe taught with traditioal way vary sigifiatly with those studets performae taught with itegrated tehology, the differees betwee the proportios are trasformed to a approximate stadard ormal distributed radom variable Z. For eah performae ategory (HD, D, C, P, F) the alulatio of the Z value is obtaied usig: Proeedigs asilite 2011 Hobart: Full Paper 1231

6 Z pˆ teho pˆ log y, p teho p log y, traditioa l, i traditioa l, Where for eah ategory of performae the orrespodig sample proportios are determied: (1) pˆ pˆ traditioal,c tehology,c X X traditioal, traditioa l tehology, teho log y (2.a) (2.b) Where X traditioal, ad X tehology, are the umber of studets i eah of the performae ategory respetively i the traditioal lass ad the tehology lass. traditioal (=100) ad tehology (=159) are the total umbers of studets who atteded respetively the traditioal teahig ad the tehology teahig. p-teholgy-p-traditioal is the stadard error of the differee betwee the two populatios proportios: p teholog y, p traditioa l, p teho log y, (1 1 p teho log y, ) p traditioa l, (1 2 p traditioa l, ) (3) However sie the stadard error is ukow, we use the pooled proportio estimate defied by: p ˆ C X traditioa l, traditioa l X teho log y, C teho log y (4) As a be see from Table 1, the results reveal that studets ted to ahieve sigifiat better performae with tehology ad the failure rate is redued sigifiatly from 37% from 14% (p value = 0.00). Table 1. Iferee about the differee betwee the traditioal ad tehology teahig usig Z- distributio Performae Category Traditioal Teahig proportio % Teahig with Teholog y proportio % Pooled Proportio Stadar d error Z stat p value High distitio ** 0.00 Distitio Credit Proeedigs asilite 2011 Hobart: Full Paper 1232

7 Pass Pass oeded Fail ** 0.00 ** The differee is sigifiat at 0.01 Chi-Square test To test the hypothesis that there is o relatioship betwee teahig methodology ad the studets performae, a hi square test has bee oduted to test the homogeeity of proportios of the various performae groups: H 0 : π HD = π D = π C = π P = π PC = π F H a : At least oe of the proportios differs tha the others. A two-way otigey table Chi-square aalysis reveals a hi-square value of 28.7 (p value =0.0) idiatig that the ull hypothesis is rejeted. Therefore there is a sigifiat variatio i the performae proportios betwee traditioal ad teahig with tehology. However, odutig a sub-hypotheses of the π D = π C = π P ould ot be rejeted with a hi-square value of 0.5 (p value =0.7) idiatig that the differee i proportios betwee these three ategories are ot that sigifiat. The sub-hypothesis π HD = π F is rejeted with a hi-square value of 27 (p value = 0.0). These results are i oordae with those determied usig Z distributio. Fator Aalysis of the survey We have employed fator aalysis as a data redutio tehique i order to defie the uderlyig struture amog the variables (item 1 to item 40). Suh tehique would group highly orrelated variables ito groups or fators whih would help us to fid patters of relatios amog the variables. Figure 2 illustrates the overall model of our aalysis. Figure 2. Overall model of the aalysis Proeedigs asilite 2011 Hobart: Full Paper 1233

8 To start this aalysis, we issued the followig SPSS ommads: Aalyze->Dimesio Redutio->Fator. Based o the 40 items orrelatio matrix, Priipal Compoet ad Varimax were seleted respetively as the extratio ad rotatio methods i the aalysis. I order to make the output easier to sa ad sie fator loadig less tha 0.5 are too small to be osidered, we suppressed the low absolute loadigs at 0.5. Aalysis results revealed that the first fator explais 44.21% of the total variae of all items. The seod fator added 7.2% to the aumulated variae ad the third fator explaied oly about 4% for a total of about 56%. Examiig the items lusterig to eah of the three fator, we olude that the first fator oers studets pereptio ad satisfatio towards the delivery of the subjet ad teaher evaluatio lusters twety oe items of the survey questios (8, 11, 14-16, 18, 19, 22, 23, 25, 27, 28, 30, 31, 33-40). The seod fator oered pereptio towards the use of tehology ad iludes 7 items (1-3, 5-6, 20, 24). The third fator osistig of 5 items (4, 12, 21, 29, 32) oers studets pereptio towards statistis. Items 9 ad 17, orrespodig to whether statistis is easier tha Mathematis ad whether the omputer lab timig was too log, did ot hag to ay of the three fators. As well the two items related to the text book did ot luster to ay fator. Note that while the results are ot sesitive to the extratio ad rotatio methods, the umber of fators retaied is very ruial. We have retaied three fators based o the Sree plot ad the iterpretability of the fators. Further, we have measured the reliability of eah of the three sets of items orrespodig to the three fators retaied. The reliability aalysis test oduted ofirmed a Crobah's alpha=0.96 for the first set of items, Crobah's alpha=0.85 for the seod set ad Crobah's alpha=0.83 for the third set. For the reliability aalysis, o item had to be reverse-saled. The values of the Crobah alphas ad their orrespodig split half oeffiiets were the same suggestig that there are o aomalies i the data survey ad that eah set measures a sigle ostrut. Based o this aalysis three ew variables were ostruted by averagig the items orrespodig to eah fator ad were labelled by delivery, tehology ad statistis. Fator Aalysis subsequet Aalysis: Oe Way ANOVA A oe way ANOVA aalysis was oduted to examie ay assoiatio betwee the three ew ostruts delivery, tehology ad statistis ad studets performae. Studets were grouped ito three performae ategories (Fail+Pass Coeded, Pass+Credit ad Distitio + High Distitio). The ategories meas for eah ostrut are ompared by ANOVA. As a be see from Table 2, the meas for the studets pereptio towards tehology differ sigifiatly (p=0.005) amog studets performae. Similarly, the meas for the studets pereptio towards statistis ireases with the respet to the performae but the differee is oly sigifiat at 5%. However studets pereptio towards the subjet delivery ad teaher evaluatio did ot deped o studets performae. Table 2. Meas ompariso as a futio of studets performae Costrut Fail ad Pass Coeded Pass ad Credit Distitio ad High Distitio F STAT Delivery (.132) Tehology (.005) ** Statistis (.05) * I parethesis are p values. Meas sale is 1-5. ** Null hypothesis is rejeted at 1%. * Null hypothesis is rejeted at 5%. Proeedigs asilite 2011 Hobart: Full Paper 1234

9 Fator Aalysis subsequet Aalysis: Disrimiat Aalysis Disrimiat Aalysis is performed to establish whether differees i pereptios (subjet delivery, tehology beefits ad Statistis) exist betwee the three studets performae groups. To start this aalysis, we issued the followig SPSS ommads: Aalyze->Classify->Disrimiat. The three ostruts are defied as idepedet variables ad the three levels studets performae as the groupig variable. Stepwise method reveals that oly the fators Tehology ad Statistis are sigifiat to predit studets performae from his/her respose to the survey. The hit ratios usig the two preditors are give i Table 3. As a be see, studets who did ot do well i the subjet were poorly disrimiated from the other groups. This is due to the fat that eve the studets who did ot perform well i the subjet had a relatively similar positive pereptio towards the delivery, the itrodutio of tehology ad Statistis. Table 3. Hit Ratio of the two preditors Performae Group Hit ratio Hit ratio with ross validatio Hit ratio by hae Fail ad Pass Coeded 5% 1% 14% Pass ad redit 30% 21 % 30% Distitio ad high distitio 90% 89% 56% Colusio The ilusio of tehology has give the studets the opportuity to apply the statistial oepts to real-world situatios. The studets leared to preset data ito frequey tables, histograms ad otigey tables with the use of Exel program. They also leared to summarize data usig Exel data aalysis tools ad to produe box plots. Furthermore, the aalysis of the survey highlighted the studets positive pereptio regardless of their overall performae ad the failure rate was redued from 34% with traditioal teahig to oly 14% with the ilusio of tehology. Overall, the survey expressed a sigifiat result showig that the use of Exel as ew tehology, studets performed muh better. The appliatio of Exel at the foudatio level of statisti will help the studets i various situatios; maximize their time i aalyzig the data ad other quatitative subjets suh as aoutig, fiae, omputer appliatio, ad maagemet deisio makig tools. Nevertheless, we must remai deliberately autious with the use of ew tehology is ot the subjet but oly tools that will help simplify the approah i statisti. Thus, we eed to keep i mid that we still have to fous o teahig the oepts of statistis ad ot the tehology used. Referees Baharu, N., & Porter, A. (2009). Teahig statistis usig a bleded approah: itegratig tehology-based resoures. I Same Plaes, differet spaes. Proeedigs asilite Auklad 2009 (pp ). Fortes, PC. & Thathae, A. (2010). Dealig with Large Classes: A Real Challege, Proedia Soial ad Proeedigs asilite 2011 Hobart: Full Paper 1235

10 Behavioral Siee, Iteratioal Coferee o Mathematis Eduatio Researh 2010 (ICMER2010). Volume 8, Garfield, J. (1995). How Studets Lear Statistis. 63 (1), Garfield, J., & Ahlgre, A. (1988). Diffiulties i Learig Basi Coepts i Probability ad Statistis: Impliatios for Researh. Joural for Researh i Mathematis Eduatios, 19 (1), Garfield, J., Chae, B., & Sell, J. L. (2000). Tehology i College Statistis Courses. Derek Holto (Ed.), The Teahig ad Learig of Mathematis at Uiversity Level: A ICMI Study, Gorma, M. F. (2008). Evaluatig the Itegratio of Tehology ito Busiess Statistis. Iforms Trasatio o Eduatio, 9 (1), Guidelies for Assessmet ad Istrutio i Statistis Eduatio (GAISE), Ameria Statisital Assoiatio. Moore, D. S. (1997). New Pedagogy ad New Cotet: The Case of Statistis. Iteratioal Statistial Review, 65, Morales, L., & Roig, G. (2002). Coetig a tehology faulty developmet program with studet learig. Campus Wide Iformatio System, 19 (2), Neiss, M. L. (2005). Preparig teahers to teah sied ad mathematis with tehology: Developig a tehologial otet kowledge. Teahig ad Teaher Eduatio, 21, Natioal Couil of the Teahers i Mathematis (NCTM), Peiris, S., & Beh, E. J. (2006, August). Where statistis teahig a go wrog. CAL-laborate, pp Peiris, S., & Peseta, T. (2004). Learig Statistis i First Year by Ative Partiipatig Studets. UiServe Siee Sholarly Iquiry Symposium Proeedigs, (pp ). Prabhakar, G. P. (2008). Stats for the Terrified: Impat of Differet Teahig & Learig Approahes i the Study of Busiess Statistis. Iteratioal Joural of Busiess ad Maagemet, 3 (6), Sam, L. C., & Kee, K. L. (2004). Teahig statistis with graphial laulators i Malaysia: Challeges ad ostraits. Miromath, 20 (2), Su, Y.-T., & Liag, C.-L. (2000). Usig multivariate rak sum tests to evlauate effetiveess of omputer appliatios i teahig busiess statistis. 27 (3), Tsao, Y.-L. (2006). Teahig Statistis With Costrutivist-Based Learig Method To Desribe Studets Attitudes Towards Statistis. Joural of College Teahig ad Learig, 3 (4), Vellema, P. F. (1995). Multimedia for Teahig Statistis. The Ameria Statistiia, 50, Appedix A Table1. Peretages, Mea ad Stadard Deviatio for eah item Items Mea SD S.D D N A S.A 1 I a orgaize data ito tables ad graphs usig Exel program I a summarize data usig omputer program I a easily iterpret the data usig Exel Program Statistis is relevat i my everyday life Workig with my lassmates i the omputer lab is very useful Usig omputer program is very helpful i problem solvig The textbook is very helpful i aswerig my assigmet Exerises give i the tutorial is very helpful I fid Statistis easier tha Mathematis Proeedigs asilite 2011 Hobart: Full Paper 1236

11 10 I reommed the textbook for Statistis related subjets The oepts of Statistis is well explaied i the leture The subjet is iterestig ad fu Oe hour leture is ot eough to disuss oepts of statistis Teahers are very helpful ad supportive Had-outs are helpful ad exellet I a ask questios ad get poits larified The tutorial ad omputer lab is too log There is a suffiiet review materials provided for the exams I a lear oepts whe my teaher gives examples based o real life situatios Computer lab sessio is better tha purely letures ad tutorials I fid statistis iterestig ad relevat I a easily lear ad apply the oepts leared i the leture usig omputer program ad i the tutorial Exerises give i the omputer lab is very helpful Usig omputer program, I a aalyze data better The teahig staff are approahable whe I eed help I fid probabilities hardest topi i Statistis The teaher gives presetatio helpful for examiatio The teaher eourages me to thik o my ow I a apply i other subjets what I have leared i Statistis My learig experiee i this subjet made me ethusiasti about further learig 31 The teahers give osultatio hours where I a reah them ad offer additioal help I a relate that Statistis is used i the real world The teahig staff gives regular feedbak The leture otes, had-outs are well-orgaized, well writte ad useful 35 The type of questios assiged for homework helps me lear the material better The midterm review leture was iformative ad helpful Proeedigs asilite 2011 Hobart: Full Paper 1237

12 37 The midterm exam diffiulty level is fair Explaatio of oepts is adequate Demostratio of solutio proess is adequate There are eough examples give per leture/tutorial Likert Sale: 1 Strogly disagree, 2 Disagree, 3: Neutral, 4- Agree, 5: Strogly Agree Author otat details: Paulie Carolye Fortes pauliefortes@uowdubai.a.ae Please ite as: Thathae, A. & Fortes, P.C. (2011). A multivariate survey aalysis: Evaluatio of tehology itegratio i teahig statistis I G. Williams, P. Statham, N. Brow & B. Clelad (Eds.), Chagig Demads, Chagig Diretios. Proeedigs asilite Hobart (pp ). Copyright 2011 Abdellatif Thathae & Paulie Carolye Fortes. The author(s) assig to asilite ad eduatioal o-profit istitutios, a o-exlusive liee to use this doumet for persoal use ad i ourses of istrutio, provided that the artile is used i full ad this opyright statemet is reprodued. The author(s) also grat a o-exlusive liee to asilite to publish this doumet o the asilite web site ad i other formats for the Proeedigs asilite Hobart Ay other use is prohibited without the express permissio of the author(s). Proeedigs asilite 2011 Hobart: Full Paper 1238