Computers ind. En~n~ Vol. 13, Nos I-4, pp.203-207, 1987 0360-8352/87 $3.00+0.00 Printed in Great Britain. All rights reserved Copyright c 1987 Pergamon Journals Ltd

Learning Style as an Influence on the Effectiveness of Self-Paced Computer-Assisted Inst ruct ion: Preliminary Results

Dept.

Gary M. Kern and Khali l F. Matta

of Management, Coll. of Business, Univ. of Notre Dame Notre Dame, Indiana 46556 USA

Abstract

Many have studied the re la t i ve effectiveness of self-paced, com- puter-assisted ins t ruc t ion. An experiment was conducted to invest i - gate whether people of certain who exh ib i t certain learning styles are more successful using a self-paced inst ruct ional package. The p r e l i - minary results reported here indicate that differences in learning performance can be iden t i f i ed for d i f fe ren t learning sty le categories (as measured by the Myers Briggs Type Indicator (MBTI).

Keywords

Computer-assisted ins t ruc t ion; self-paced ins t ruc t ion; t ra in ing.

Introduction

The effectiveness of computer-assisted instruct ion (CAI) has been a focus for numerous researchers. Early studies contrasted the bene- f i t s of CAI with t r ad i t i ona l classroom inst ruct ion (see, for example, Ebner, et a l , 1984; Johnson, et a l , 1986; Smith, 1983, and Thomas, 1979). The results were inconsistent and inconclusive with some indicat ing higher levels of achievement with CAI while other studies showed no s ign i f i can t differences {Kul ik, et a l , 1980). The d i f fe ren- ces in results can be a t t r ibu ted to small sample sizes, differences in effectiveness measures (cost, level of retent ion, compression of time, etc) . Clark (1983) argues the differences may be the resul t of a novelty ef fect when using CAI and/or topic and style rather than medium.

More recently, researchers started to address the environmental influences on CAI effectiveness. Some have also t r i ed to ident i fy the character is t ics of CAI which may cause improvements in student perfor- mance. Tsai and Pohl (1981) studied the ef fect of supplementing CAI with varying degrees of inst ructor involvement. Gay (1986) studied the impact of student control of inst ruct ion - the use of self-paced inst ruct ional packages.

One factor of int , .... st is the student's personal i ty tyue and how that may ef fect student ?erformance. The general hypothesis is that certain persona]ity ty~es w i l ] be more successful than others when asked to learn using a self-paced inst ruct ional medium. This paper presents preliminary results on the effects of personal i ty type on the effectiveness of self-paced inst ruct ion.

Method

Personality Type

Personality type can be measured using the Myers Briggs Type Indicator (MBTI). The MBTI is an instrument used to assess personal i- ty type for over t h i r t y years in many varied research endeavors. Recently i t was used to obtain a p r o f i l e of those workers involved in data processing and computing (Sit ton and Chmelir, 1984; Bush and Schkade, 1985; and L/~ns, 1985).

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204 Proceedings of the 9th Annual Conference on Computers & Industrial Engineering

The MBTI instrument is based on Jung's personal i ty type theory which c lass i f ies people according to t he i r preferences on four scales:

- extroversion (E) vs introversion (1) - sensing (S) vs i n t u i t i o n (N) - thinking (T) vs feel ing (F) - judging (J) vs perceiving (P)

A detai led explanation of these scales can be found elsewhere (see Myers, 1985).

Subjects

Students in the College of Business at the University of Notre dame par t ic ipated in the study. The students are current ly enrolled in one of the College's four degree programs: Bachelor, Master of Business Administration (MBA), Executive MBA, or Master of Science in Administration (MSA). The number of students who have par t ic ipated in the experiment to date exceeds 110. Table I presents presents selec- ted demographic information for the sample whose resul ts are reported in th is paper. I t is important to note the var iety in age, sex, and background among the par t ic ipants .

Materials

All students were faced with the same learning experience. They received inst ruct ion in Lotus 123, one of the most popular spreadsheet software packages current ly avai lable for the microcomputer. The students were taught using a self-paced videocassette ins t ruc t iona l package developed by the accounting f irm of Arthur Young and Company.

Each student received ind iv idual ized inst ruct ion and had com- plete control over the pace of presentation. They worked with a videocassette player, a microcomputer, and two monitors (one displayed the videocassette mater ia l , and the other acted as the computer's monitor).

Procedure Each subject was pretested using the MBTI. Demographic informa-

t ion was also col lected at that time. The subjects then studied the basic functions and procedures involved in the use of Lotus 123. The students each viewed the videocassette segments and per iod ica l l y pre- pared exercises cal led for in the package. They were able to perform the exercises on the microcomputer in the ins t ruc t iona l workstation.

The students were free to review any segment of the videocassette as many times as they wished. They had complete freedom to stop and pract ice speci f ic functions un t i l they achieved a level of comfort with Lotus 123. No time l i m i t was imposed for completion of the course. However, they were asked to complete the posttest examination as soon af ter the i r ins t ruct ion as possible. The posttest results were tabulated and analyzed in terms of the demographic variables and the MBTI scores.

Dependent Variable

The dependent var iable was the subject 's score on a 40-item mul t ip le choice examination administered upon completion of the lear- ning experience. The questions ranged in d i f f i c u l t y from simple function key i d e n t i f i c a t i o n to more complex tasks that asked the student to make extensions from the i r learning experience. The scores were then aggregated according to the various demographic and MBTI c lass i f i ca t ions for analysis.

Results

Table 2 presents the average examination scores (out of a possib- le 40) for selected demographic c lass i f i ca t ions . Note that niether gender differences nor pr io r computing experience had a s ign i f i can t impact on the performance resul ts.

A noteworthy resul t from the demographic analysis indicates that students with pr ior exposure to "canned" software in general and computer spreadsheets in pa r t i cu la r performed better than other groups. This resul t tends to support the f indings of Gay (1986) that students with p r io r kowledge of a topic are bet ter able to succeed in a self-paced educational environment.

Kern and Matta: Learning style 205

Tables 3 through 6 present the posttest scores in re lat ion to MBTI categories. No d~fference in performance is noted between those individuals judged extroverted and those judged introverted. The judging-perceiving scale also appeared to have l i t t l e ef fect on post- test performance.

Students that showed a preference for "thinking" performed 10-15% better than those students who had a preference for " fee l ing. " The same pattern was found for the fourth and f ina l MBTI scale: In tu i t i ve types performed better than sensing types. Students whose personali- t ies combine thinking and in tu i t i on performed 30% better than those whose personal i t ies combine feel ing and sensing (please refer to Table 7).

Dis(uss~on of Res~!ts

Table 8 contains some br ie f descriptions of the characterist ics attr ibuted to each MBTI category (Taggart and Robey, Ig81). The NT ( in tu i t i on / th ink ing ) type tends to be re la t i ve l y logical and imperso- nal; this person finds impersonal analysis of a technical or theore- t i ca l nature appealing. The SF (sensing/feeling) type finds interper- sonal interact ion important.

Taken ind iv idua l ly , the four type categories (thinking, in tu i - t ion, sensing, feel ing) have characterist ics that predict the results that were found. As mentioned above, the thinking types tend to prefer analysis and do not express a need for interpersonal interac- t ion. People c lassi f ied as feel ing express a greater preference for such interact ion. These people would prefer greater interact ion in the education process.

People c lassi f ied as i n t u i t i ve enjoy learning new sk i l l s , but they impatient with routine. The lack of a specif ic scheduling in self-paced learning appeals to the manner in which they prefer to work: bursts of ac t i v i t y with intermit tent slack periods. People who are c lassi f ied as sensing types prefer to apply previously-learned sk i l l s . Routine and f am i l i a r i t y appeal to them.

The preliminary results reported here show that the NT type of student performed s ign i f i can t ly better than average using self-paced instruct ion. These results can be attr ibuted in part to thei r motiva- t ion ( thei r desire to learn a new s k i l l ) . In addit ion, the time frame of self-paced learning suits this group's preference for work style. The SF types performed s ign i f i can t ly below average using the same instruct ional package. This can be attr ibuted to thei r need for interpersonal interact ion which is not f u l f i l l e d by the current design of the self-paced instruct ional package. This group tends to prefer applying previously-learned sk i l l s rather than aquiring new ones.

Conclusion

This experiment found that personality type tends to af fect a student's success with self-paced instruct ion. Future substantiation of these preliminary results would indicate that specif ic learning methods should be used with students who exhib i t certain personality types. A pretest similar to the MBTI could be used to categorize students. They could then be assigned to learn a common topic using one of the several educational media currently avai lable (classroom, computer-based tu to r ia l s , interact ive video instruct ion, etc.) .

A second interpretat ion of these preliminary results addresses the environment in which the self-paced package is u t i l i zed . Like Tsai and Pohl (1981), this experiment has found that teacher/student interact ion may be important to the success of self-paced instruct ion. In this case, specif ic types of students exhibited a need for addi t io- nal interact ion with an instructor. Incorporating such interact ion into future implementations of self-paced instruct ion may affect stu- dents' learning success because of certain students' need for such interact ion.

206 Proceedings of the 9th Annual Conference on Computers & Industrial Engineering

Future research should expand the subject pool in an e f fo r t to develop stat is t ica l ly-supported conclusions about these findings. Additional self-paced instruct ional packages should be studied. The packages should attempt to study student response to d i f ferent topics of instruct ion. By categorizing students, future teachers may find that they can introduce students to instruct ional media that w i l l provide those students with the greatest potential for learning s u c c e s s .

R e f e r e n c e s

I . Bush, CM, and LL Schkade, "In Search of the Perfect Programmer." Datamation, March 15, 1985, p. 128+.

2. Clark, RE, "Learning From Computers: Theoretical Problems." Presented at the Annual Meeting of the American Educational Research Association, New Orleans, LA. Apr i l , 1983

3. Ebner, DG, DT Manning, FR Brooks, JV Mahoney, HT Lippert, and PM Palson, "Videodiscs Can Improve Instruct ional Eff ic iency." Instruct ional Innovator, vol. 29 (1984), no. 6, pp. 26-28.

4. Gay, G, " Interact ion of Learner Control and Prior Understanding in Computer-Assisted Video Instruct ion." Journal of Educational P ~ h o ~ , vol. 78 (1986), no. 3, pp. ~ T .

5. Johnson, JF, CR Crowell, and E Steck, "Comparing the Instruct ional Effectiveness of CAI With Tradit ional Methods in the Freshman Chemistry Laboratory." NECC '86: Seventh National Educational Computing Conference, 1986.

6. Kulik, JA, CC Kulik, and PA Cohen, "Effectiveness of Computer- Based College Teaching: A Meta-Analysis of Findings." Review of Educational Research, vol. 50 (1980), no. 4, pp. 525-546.

7. Lyons, ML, "The DP Psyche." Data~a.t~o~, August 15, 1985, pp. 103+.

B. Myers, IB, Introduction to ~2e. Consulting Psychologists Press, Inc., Palo AIto~-CAT'~-T~

9. Sitton, LS, and G Chmelir, "The In tu i t i ve Computer Programmer." D~a~at~on, October 15, 1984, pp. 137 ÷.

10. Smith, RC, "Full Scale Pi lot Testing of Florida's Videodisc Training Project." delivered at the Conference On Interact ive Instruction Delivery, Orlando, FL, 1984.

11. Taggart, W, and O Robey, (On the Dual Nature of Human Information Processing and Management. A~ad~m~ of Mana~emen~ Review, vol. 6 (1981), no. 2, pp. 187-195.

12. Thomas, DB, "The Effectiveness of Computer Assisted Instruction in Secondary Schools." AEDS Journal, vol. 12 (1979), pp. 103-116.

13. Tsai, SW, and NF Pohl, "Computer-Assisted Instruction Augmented With Teacher/~tudent Contacts." ~ournal of Experimental Education, vol. 49 (1981 no. 2, pp. 120-12 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Kern and Matta: Learning style 207

TABLE I DEMOGRAPHICS

AGE SEX PROGRAM 18-19 30.8% Male 76,4% Undergrad 34.9% 20-29 21.5% Female 23.6% MBA 16o0% 30-39 37,4% Exec MBA 37.7% 40-49 7,5% MSA 11.3% 50-59 5,6%

AVERAGE AGE: 29

AVERAGE # OF COMPUTER COURSES: 2.03

TABLE 2 PREVIOUS EXPERIENCE VS NO PREVIOUS EXPERIENCE SCORES

Personal Computer Mainframe Programming Canned Software Spreadsheet

Undergrd MBA S NUMBER 18 I S AVERAGE 22 .56 30,00

N NUMBER 17 3 N AVERAGE 23 .71 27.67

Undergrd MBA E NUMBER 18 2 E AVERAGE 23 ,33 30.00

I NUMBER 17 2 I AVERAGE 2 2 . 8 8 26.50

Undergrd MBA T NUMBER 15 I T AVERAGE 23 .80 30.00

F NUMBER 20 3 F AVERAGE 2 2 . 6 0 27.67

PREVIOUS EXPERIENCE NO PREV EXPERIENCE

# AVE SCORE # AVE SCORE 72 26 45 24 52 26 65 24 59 24 58 26 59 27 58 23 3B 27 79 24

TABLE 3 S VS N SCORES

EXEC MSA OTHER UNKNOWN *TOTAL* 16 6 3 0 44

25.88 2 2 . 8 3 20.67 23.84

24 4 0 3 51 27,96 26,00 24,33 26.]6

TABLE 4 E VS I SCORES

EXEC MSA OTHER UNKNOWN *TOTAL* 17 3 2 2 44

26,g4 2 4 . 0 0 21,50 25.05

23 7 I I 51 27.26 24.14 24.00 25.12

TABLE 5 T VS F SCORES

EXEC MSA OTHER UNKNOWN *TOTAL" 29 6 2 3 56

27,62 2 7 . 0 0 23,00 26.23

I I 4 I 0 39 25.82 19.75 ERR 23.44

Undergrd MBA J NUMBER 17 2 J AVERAGE 2 2 . 5 3 28.00

P NUMBER 18 2 P AVERAGE 2 3 . 7 8 28.50

Undergrd MBA SF NUMBER I0 I SF AVERAG 2 1 . 6 0 30.00

TN NUMBER 7 I TN AVERAG 2 3 . 8 6 30.00

TABLE 6 J VS P SCORES

EXEC MSA OTHER UNKNOWN *TDTAL* 17 6 3 2 47

40.18 2 4 . 5 0 20.67 29.21

15 3 0 I 39 26.80 24.67 31.00 25,44

TABLE 7 SF VS TN SCORES

E~EC MSA OTHER UNKNOWN *TOTAL* 5 2 1 0 19

22.20 1 2 , 0 0 16.00 20.89

18 2 0 3 31 27.67 24,50 24.33 26,35