Computer Technology for Recording, Storing, and Analyzing Temporal Data in Physical Activity Settings

Transcription

1 JOURNAL OF TEACHING IN PHYSICAL EDUCATION, 1984, 4, Computer Technology for Recording, Storing, and Analyzing Temporal Data in Physical Activity Settings B. Robert Carlson and Thomas L. McKenzie San Diego State University Data gathering for research on teaching in physical education appears to be heading into a new era, an era in which electronic data collection tools will merge with older measurement techniques to make the processes of storing, analyzing, and transporting data more efficient. The rapid development of microcomputing technology has reached the stage in which portable computers are now practical as state-of-the-art tools for on-site research projects. This article addresses one of the most critical problems for doing research on teaching using time based variables. In the past, when duration recording was the observational technique, there were two ways to collect data: either through multiple stop watches or through interval recording. Both methods have their limitations-one in the manipulation of the several watches and the other in converting interval data to accurate units of time. Outlined in this article is a microcomputer program for on-site duration coding, data analysis, permanent storage, and mainframe support for research on teaching physical education. The system is complex by design but practical to use. It produces total observation time, total time by category, frequency by category, mean length of occurrence, and the percent of total time each category was observed. Interest in studying exercise and sport pedagogy has been rekindled through recognition of time as an important factor in learning. Through direct observation of students and teachers in the natural setting, researchers in general education (Berliner, 1979; Denham & Lieberman, 1980) and researchers and practitioners in physical education (Anderson, 1980; Pieron & Cheffers, 1982; Siedentop, 1982; Templin & Olson, 1983) have found temporal factors to be germane to learning and teaching. Direct observation procedures involve using trained observers to observe, encode, and record the behavior of participants. This recent interest in time has led to a proliferation of observation instruments for use in studying studentslathletes and teacherslcoaches (Darst, Mancini, & Zakrajsek, 1983). In direct observation, researchers typically use one or more of the standard observation techniques-frequency, duration, interval, andlor placheck recording (Siedentop, 1982). Duration recording is the preferred technique for focusing on the temporal dimension of a discrete behavior, one that has a clear beginning and ending (Johnston & Pennypacker, 1980). Duration recording permits assessment of the length of time a classlteam, studentlathlete, or teacherlcoach engages in a particular behavior. Duration recording is also used for time-to-criterion measures, such as the time necessary to complete a 1.smile run. Latency, the time period between the presentation of a stimulus (e.g., a request) and

2 COMPUTER TECHNOLOGY 25 the response (e.g., student compliance), is measured by timing in a manner similar to duration recording. Exercise and sport pedagogy researchers have organized duration data in two different ways, mean duration and cumulative duration. Mean duration is frequently used when the behavior of interest occurs with some regularity, as when McKenzie and Liberatore (1984) studied the average transition time between innings of softball games under three different game contingencies. Similarly, Siedentop, Rife, and Boehm (1974) examined the effectiveness of a "good behavior game" on reducing the average length of managerial episodes in classes taught by student teachers. Cumulative duration recording is useful when the researcher is interested in the total time an observed behavior occurs during a specified time period. Examples include how much class time is allocated for management, instruction, and active learning (Siedentop, 1982), how mainstrearned handicapped and nonhandicapped students use class time (Aufderheide, McKenzie, & Knowles, 1982), and how much time a teacher spends on different instructional strategies (McKenzie, Clark, & McKenzie, 1984). Siedentop, Tousignant, and Parker (1982) indicated that duration recording was a particularly useful procedure for measuring academic learning time in physical education settings (ALT-PE). With the limitations of instrumentation for inputing, outputing, and analyzing duration data in physical activity settings, few researchers have used this method. Rather than using duration recording which involves continuous measurement, researchers have opted to use interval recording. The most common interval protocol is to observe a subject during one interval (perhaps 6 seconds) and record the observation data during the next interval, a form of discontinuous time-based measurement. By using intervals of constant length, researchers have been able to estimate the time for different behavior categories. However, interval recording has been questioned as a research technique because it may incorrectly estimate the duration of a behavior and does not provide information on the frequency of occurrence of a behavior (Alexander, 1983; Johnston & Pennypacker, 1980; Springer, Brown, & Duncan, 1981). Most exercise and sport pedagogy researchers who wanted to collect duration data have had to do it with only pencil, paper, and stopwatches. Limited in this way, they could study only a few events unless many stopwatches were used concurrently. When using these tools, the duration of the individual response had to be recorded on paper, which mandated a constant shifting of attention from the activity to the data sheet. Consequently, data were often inaccurate or were missed entirely. After the observation period, computations to translate the data were done by hand, which was frequently quite a slow and tedious process. Existing equipment has also restricted the scope of research hypotheses capable of being tested. Not only are researchers interested in the duration or temporal extent of an event, they are also interested in its temporal locus-the point at which a response occurs (Johnston & Pennypacker, 1980). Researchers are also interested in the frequency or recurring nature of a response and often have been interested in studying behaviors that occur simultaneously. To make the collection of duration data feasible, a more efficient system is needed. The need to be more efficient and to collect more complex data led several exercise and sport pedagogy researchers to computerized observation systems. Electronic behavioral processors such as the Apple II (Cicciarella & Martinek, 1982), Datamyte (Hawkins, Wiegand, & Bahneman, 1983), TICOR-DAC (Wuner, 1983), and the SSR-7 (Gray, 1983) have been used to study duration of behavior in physical activity settings. Unfortunately, these systems have one or more of the following drawbacks: high cost,

3 26 CARLSON AND MCKENZIE no portability, limited long-term storage, and no immediate analysis of the data. McKenzie and Carlson (1984) have explored the efficacy of using relatively inexpensive, portable (briefcase model) computers for exercise and sport pedagogical research. They identified the following characteristics of a computer as important for researching pedagogical questions in physical activity settings: (a) operates in the field as well as the laboratory, (b) communicates data fies to external storage devices, (c) is reasonable in cost, (d) has a full keyboard, (e) has an internal clock in the hardware, (0 has a video screen, (g) has industry support for the hardware and software, and Q has the ability to correct errors before data analysis. Several computers were evaluated; the Radio Shack TRS-80 Model 100 with 24K memory was selected for use in gathering interval data. With this computer in mind, the following discusses software for collecting and analyzing duration data. Computer Program Development The basic software program is relatively simple in design but sufficiently powerful in function to provide the measures demanded by pedagogical researchers. The program records the beginning and ending of behaviors in real time and may record five behaviors simultaneously. These temporally overlapping behaviors could occur within a single subject (e.g., a teacher simultaneously providing manual assistance to one student while monitoring the whole class) or among different subjects in the same settings (e.g., a teacher and a student; two or more different students). The sample program extends the duration research designs currently found in the literature. Although only five behaviors may be recorded simultaneously, a total of 25 different behaviors may be recorded during an observation period which may be of any length. This program provides storage for three data files. Any of the fies may be reopened for recording additional data. To collect data, a coded keyboard letter is depressed at the beginning of the behavior and is again depressed at the ending of the behavior. When the observer elects to halt data collection, a "Z" is depressed and the data entry portion of the program is terminated. Data collection is limited to 250 observations per file. Errors in coding may occur during data collection and, because data are automatically stored in a data fie, these errors cannot be corrected immediately. If an error is made, however, the observer can note the exact time of the error on the digital clock in the upper right comer of the screen and correct the behavior through the computer's text editing function after completing the observations but prior to data analysis. The data for a single observation are stored on a single line, with the behavior code being followed by a space and the real time in hours, minutes, and seconds. This format permits easy visual inspection by the observer after data collection. Thus, it is not necessary to handle the data again after it has been entered at the keyboard because all computations and communication can be done electronically. Once data collection has ended and any errors are corrected, the program can present the following data for each occurrence of any measured behavior: behavior name, beginning time, ending time, length of behavior, and percent of total time. The program also summarizes the data for the different behavior categories: (a) total time of observation, (b) total time for the behavior category, (c) frequency of occurrence, (d) average length of each occurrence, and (e) percent of total time during which that behavior category

4 COMPUTER TECHNOLOGY 27 Table 1 Sample Program Output for Observation A OBS ONSET END LENGTH 010 Total time for file = 60 Total time for A = 44 Number of occurrences for A = 3 Average time for A = Percent of total time =,7333 was observed. If additional data are added to the file at a later time, all results are recalculated. During the analysis of large amounts of data, the results scroll off the small eightline screen of the computer. Therefore, the results are written to an output file to be retained for later detailed analysis. All data and output reports are written to separate ASCII files up to a maximum of three data files and three output files. The ASCII format permits easy transfer of the data via a modem to a remote computer or directly to a cassette recorder or printer. Should the researcher desire to merge files, the merger would normally be done after the transfer of files to the remote computer facilities. In the remote computer, data can then be analyzed using more complex data analysis software packages (SPSS, BIOMED, etc). Field Tests To examine the efficacy of the computer hardware and the sample software program in the field, a physical activity was evaluated using conventional equipment and the computer. In a test lasting 90 minutes, the accuracy of the computer timing was verified. Post-observation data analysis by machine and by hand resulted in 100% accuracy for all computation procedures included in the software. No errors were made during data entry. When testing different size data files, the storage capacity of the computer became a factor. Collection of the data functioned normally but, as the size of the data file increased, the time needed to perform the analyses also increased. Analysis of a behavior in a data file containing 78 observations took 15 seconds, while the same analysis in the data file of 150 observations took 100 seconds. When the number of observations in a file exceeded 150, the memory capacity of the TRS-80 Model 100 computer with 24K memory was exhausted. To analyze data files exceeding 150 observations, a computer with a larger memory capacity is needed.

5 CARLSON AND MCKENZIE System Adaptability While the program is designed to collect data according to a specified protocol, the software may be used for many different protocols. Variations for duration data collection include number of variables and number of observations. Any number of variables (1-5) may be observed simultaneously. Should more variables or observations be required, modifications in the software can easily be made. While many different data collection programs can be written for this computer, storage constraints limit the number of programs stored in the computer at any one time. Other programs can be permanently stored in a remote computer or on cassette tape and can be easily transferred for use in the field. Final Comments This paper has addressed the use of the portable computer as a research tool, one that can gather duration data more easily and more accurately than through traditional procedures. The hardware and software system described is a low-cost microcomputer developed to record, store, analyze, and transmit relevant frequency and duration data, while providing immediate output in the physical activity setting. The system is relatively inexpensive (approximately $1,000), fully portable, and useful for researching a variety of exercise and sport pedagogy questions involving studentlathlete and teacherlcoach behavior. Hawkins et al. (1983) have stressed the importance of electronic timing devices in micro-teaching and field experiences of undergraduate physical education majors. The instrumentation described not only has value for research but also has potential for use in preservice and inservice training programs. References Alexander, K. (1983). Beyond the prediction of student achievement: Direct and repeated measurement of behavior change. In P. Dodds & F. Rife (Eds.), Time to learn in physical education: History, completed research, and potential future for academic learning time in physical education. Journal of Teaching in Physical Education, Monograph 1, Anderson, W.G. (1980). Analysis of teaching physical education. St. Louis: Mosby. Aufderheide, S., McKenzie, T.L., & Knowles, C. (1982). Effects of individualized instruction on handicapped and nonhandicapped students in elementary physical education classes. Journal of Teaching in Physical Education, 1 (3), Berliner, D. (1979). Tempus educare. In P. Peterson & H. Walberg (Eds.), Research on teaching (pp ). Berkeley, CA: McCutchan. Cicciarella, C.F., & Martinek, T.J. (1982). A microcomputer program for real time collection and immediate analysis of observational data. Journal of Teaching in Physical Education, 2(1), Darst, P., Mancini, V., & Zakrajsek, D. (Eds.) (1983). Systematic observations instrumentaiion for physical education. West Point, NY: Leisure Press. Denhan, C., & Lieberman, A. (Eds.) (1980). Time to learn. Washington, DC: National Institute of Education. Gray, J.A. (1983). The dance teacher: A computerized behavioral profile. Journal of Physical Education, Recreation, and Dance, 54(9), Hawkins, A., Wiegand, R., & Bahneman, C. (1983). The conceptual nature of ALT-PE and its use in an undergraduate teacher preparation program. In P. Dodds & F. Rife (Eds.), Time

6 COMPUTER TECHNOLOGY 29 to learn in physical education: History, completed research, and potential future for academic learning time in physical education. Journal of Teaching in Physical Education, Monograph 1, Johnston, J., & Pennypacker, H. (1980). Strategies and tactics of human behavioral research. Hillsdale, NJ: Erlbaum. McKenzie, T.L., & Carlson, B.R. (1984). Computer technology for exercise and sport pedagogy: Recording, storing, and analyzing interval data. Jounurl of Teaching in Physical Education, 3(3), McKenzie, T.L., Clark, E.K., & McKenzie, R.E. (1984). Instructional strategies: Influence on teacher and student behavior. Journal of Teaching in Physical Education, 3(2), McKenzie, T.L., & Liberatore, J. (1984). Effects of three game contingencies on transition times in softball. Study in progress, San Diego State University. Pieron, M., & Cheffers, J. (Eds.) (1982). Studying the teaching in physical education. Liege, Belgium: AIESEP (International Association for Physical Education in Higher Education). Siedentop, D. (1982). Developing teaching skills in physical education. Palo Alto, CA: Mayfield. Siedentop, D., Rife, F., & Boehm, J. (1974). Improving managerial efficiency of presewice physical education teachers. Unpublished manuscript, The Ohio State University. Siedentop, D., Tousignant, M., & Parker, M. (1982). Academic learning time-physical education: Coding manual, 1982 revision. Columbus: The Ohio State University, School of Health, Physical Education, and Recreation. Springer, B., Brown, T., & Duncan, P. (1981). Current measurement in applied behavior analysis. The Behavior Analyst, 4(1), Templin, T., & Olson, J. (Eds.) (1983). Teaching in physical education. Champaign, IL: Human Kinetics. Wurzer, D.J. (1983). Correlation between Academic Leaming Time-Physical Education and student achievement in cardiopulmonary resuscitation. In R. Telarna (Ed.), Research in School Physical ). Jyvaskyla, Finland: Foundation for Promotion of Physical Culture and Health. A copy of the computer program may be obtained from either author by writing to the Department of Physical Education, San Diego State University, San Diego, CA