for Teaching General Chemistry Lance E. Jackman and Wavne P. Moellenbem Department of Educational Foundations, The University of New Mexico, Albuquerque, NM 87131 G. Dana Brabson Department of Chemistty, The University of New Mexico, Albuquerque, NM 87131
Many different instructional strategies have been developed and tried or a t least suggested for teaching general colleee chemistrv in the lahoratorv. Unfortunatelv. verv few attempts to ev3uate in a scientific way an in&&ional method's effectiveness can be found in the literature. Briefly, this study was designed to determine the relative effectiveness of different instructional approaches on college chemistry lahoratory achievement. More specifically, three instructional methods (traditional. learnins cvcle, and computer simulation) for teaching spectrophot&netry a t the freshman level were investigated. The Tradlllonal Approach The traditional approach to teaching chemistry in the laboratory involves having students perform teacher-structured lahoratorv exercises or ex~eriments.Each exneriment is designed to illustrate certain properties of atoms and molecules or sometimes just lahoratory techniques. Each step of a procedure is carefully described and students are expected to follow the procedures exactly. Usually, little is left to the students' thought or ingenuity. This kind of highly structured lahoratorv exercise is often called a verification laboratory experiment. The Learning Cycle Another approach uses discovery or guided inquiry as the underlying pedagogical framework. By studying written descriptions, pondering data, and planning experiments, a student discovers (more nreciselv. rediscovers or reconstructs) properties and ;elatiokhips. Perhaps the most widely used discovery approach in chemical education is the learning cycle, which has been described in detail by many educators (1-4). The three phases of the learning cycle-exploration. invention, and discovery (applications)-are designed to foster concept (relationship or principle) attainment as well as cognitive or intellectual growth and development ( 3 , 4 ) . While several descriptions of learning cycles in chemistry teaching have appeared, very few studies have attempted scientific evaluation of the learning cycle's effectiveness. Nevertheless, indications are that the learning cycle is an effective method for chemistry lahoratory instruction (5,6) and mav he ~articularlvuseful to concrete students struegling with adstract or fir ma^ concepts (1).
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794
Journal of Chemical Education
Computer Slmulatlon Another way to teach lahoratory chemistry is with the aid of computers. In the lahoratory, computers are being used in at least three general ways: compukr-assisted instruction (CAI), computer-managed instruction (CMI), and computer-assisted learning (CAL). These teaching applications for the computer have been discussed in more detail in articles puhlished in this Journal (7-11). Unfortunately, a void exists in the education literature: few research studies have been designed t o determine the effectiveness of computerized chemistry teaching. Most of the studies have investigated the PLATO system (12-17). While the few research studies do not provide unambiguous and indisputable evidence, indications are that PLATO provides effective simulations and that students enjoy using them. Research Deslgn The following research design was used for the evaluation: The effects of lahoratory instructional method on a spectrophotometry achievement test were measured using an analysis of covariance (18). A pretest-posttest design was incorporated to control for ~reviousknowledge concerning spectrophotometry. An a n b s i s of covariance is well suit&to this design because the variability in posttest scores that is shared with pretest scores can~hep&tialed out or removed. Thus, posttest scores are adjusted for initial differences in snectro~hotometwknowledee " as measured hv the ~ r e t e s t . The adjustment increases the sensitivity of the analysis by removing extraneous variahilitv (accounted for hv the metest) from the posttest scores (i9). For this evaluation the oo~ulationwas students taking CHEM 122L, second-semester General Chemistry ~ a h o r a ; tory, at the University of New Mexico. Approximately 300 suhjects were involved. Most of the suhjects were freshmen (50.7%). followed hv sophomores (30.9%), iuniors (10.8%), and seniors (4.9%): ~ r a d u a t estudents or-nondeqee students accounted for the remaining subjects (2.1%).A small fraction of suhjects did not report class year (0.7%). Engineering majors, including biomedical, chemical, civil, computer, electrical, mechanical, and nuclear, accounted for 51.4% of the suhjects. Physical.science majors, including
astronhvsics. . . . hioloev. ... . hiochemistrv. .. chemistrv. computer science, geugruphy, geology, math, physirs, physical science, and forestr\.. .. arcuunted t'or 25.0'? of the suhiects. The health sciences, consisting of dietetics, medical technology, pharmacy, physical education, physical therapy, predental, and pre-medical majors, accounted for 14.6% of the suhjects. A smaller number of business, education, English, and psychology majors (3.1%) and undecided or noireporting suhiects (5.9%)made up the remainder of the sample. The mean GPA-for the sample was 3.0, and reported GPA's ranged from 1.6 to 4.0. The mean age of the sample was 21.6 years, and ages ranged from 17 to 42. Median age was 19.5 years. There were 62.5% male subjects and 35.8% female suhiects: 1.7%of the suhiects did not renort sex. The rl&e levelsfor the independent variable.\instructional method1 were r l ) traditional lahnmtors experiment, 1'2) learning cycle, and (3) computer simulation. s he dependent variable was soectroohotometrv achievement as measured by a d j ~ ~ i ts(ores ~ v l on u pocttreatment achievement rrst. The, trilditi~,nuls~ectrophorumetryexperiment wui taken from one of the author's iahoratoryma&al (20) and modified slightly to fit the typical instructional format used in CHEM 122L (21). This experiment is a typical, highly structured, verification-type lahoratory exercise. Students are presented with spectrophotometric principles; then in the lahoratory, they prepare a standard curve and use it for the quantitative determination of an unknown. Ryan, Robinson, and Carmichael (22) have described a learning cycle for a spectrophotometry experiment that formed the basis for the learning cycle used in this study. In the exploration phase, students observed colored solutions of different intensities and then prepared and ranked the intensity of several stock solutions. During invention, students looked for relationships between color and other variables. The obvious intent was to have students demonstrate exprrime~~telly that inrensity is related to the concentration nFah;orhing molerulesor, more prerisrly, that al~sorhanceis directly pnq~
