2001 Conference Proceedings

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DATA AT YOUR FINGERTIPS: HIGH TECH DATA COLLECTION TECHNIQUES

Mindee O’Cummings
Glen Wilson
Karen Burstein
Arizona State University

Within the past five years, advances in semiconductor technology have brought about a whole new paradigm of personal mobile computing with pocket-sized handheld devices known as Personal Digital Assistants (PDAs). PDAs are available with varying feature sets from several manufacturers: Casio, Compaq, Hewlett-Packard, Palm Computing, and Sony as well as others. PDAs are extremely popular in the business world and are just now beginning to show up in educational settings as more and more educators begin to understand their benefits and find more uses for them. PDAs have been primarily used as information resources; that is, people use them to keep track of their calendars, find addresses and phone numbers, run their to-do lists, and jot down notes—wherever they are, whenever they need to. However, unlike calculators and limited function organizers, many PDAs today are fully programmable computers that can be used to run learning games, collect data from sensors, and move data and information over the Internet. One specialized function at which PDAs offer a great deal of promise is in the collection of data out in the field.

The Data Collection Task

Data collection in the field is a sometimes messy and cumbersome business. Difficult-to-use instruments, numerous loose papers, tedious and time-consuming data entry all make the data collection task problematic and inefficient. Examples of data collection tasks include the collection of self-reported data such as surveys and interviews, and the gathering of observational data such as descriptive, inferential, and evaluative behavioral observations. Some of the general characteristics of data collection in the field include the following:

These characteristics combine to makes difficult demands on potential solutions for field data collection situations. Traditional paper-based methods of data collection tend to be inefficient and problematic for a number of reasons such as: In many research projects, researchers spend a great deal of time collecting information with paper forms, and then later transcribing the data into a computer-database for analysis and storage. This is inefficient, tedious, and with each additional step, increases the number of errors that creep into the data. Data collection applications that can store data electronically and load it into databases directly save time and money, reduce data collection and transcription errors, and allow analysis to begin as soon as the data is transferred to the database.

Using a PDA, and in our case, a Palm, addressed many of the problematic characteristics endemic to data collection in field environments. The Palm provided the functionality we needed at a low cost and in a small, unobtrusive, package. The Palm, in our day-to-day experience, was very rugged, had a relatively long battery life, and was usable in extreme environments. The Palm utilizes a touch-sensitive, graphical user interface that is very easy to master. The development of data collection forms and instruments with a forms creation software package (Pendragon Forms) was a simple task, even for those without any prior programming experience. A Palm PDA can store a large amount of data and easily transfer the data to an off-the-shelf spreadsheet or database program, such as MS Excel or Access.

Data Collection for Special Education Teachers

According to Smith (2001), special education teachers should collect data for three primary reasons: 1) it is required by Individuals with Disabilities Education Act of 1997 (IDEA-97), 2) it is considered best practice, and 3) it allows teachers to monitor the effectiveness of their teaching methodologies. IDEA-97 requires that assessment and data collection procedures be used in three contexts: 1) for identification and qualification of students for special education services, 2) to evaluate which instruction practices are most appropriate, and 3) to monitor students’ progress on annual goals and objectives.

As a best practice, accurate student data collection will allow teachers to make informed, objective decisions about student instruction, student placement, and the effectiveness of teaching methods.

The monitoring of students’ academic progress or classroom behavior is best accomplished through accurate data collection. Various measures may be used to collect student data. For example, frequency, percent, rate, duration, latency, and magnitude are common behavioral observation measures. (See Table 1 for descriptions). Traditional recording strategies include permanent products, event recording, duration recording, response latency, and interval recording. (See Table 2 for descriptions).

Walton (1985) states that there are two principal reasons why teachers do not collect data in their classrooms: 1) they do not feel that they have enough time and 2) they do not feel that they obtain sufficient benefits compared to the effort required to gather the data. 76 percent of special education teachers do not feel that they have enough time to collect data and 46 percent believe that data collection will not aid them in improving their teaching (Walton, 2001). Use of Personal Data Assistants (PDAs) in field data collection situations can make it easier to collect data, thus, helping teachers to meet their responsibilities as well as making available data on their students’ academic progress and behavior.

Benefits to Using PDAs for Data Collection

What are the benefits to using PDAs for field data collection as compared to traditional paper-based methods? Using a Palm IIIc PDA and Pendragon Forms software on a relatively simple secondary data collection task (14 items from medical files), we achieved about a 56 percent reduction in person/hours compared to recording data on paper and later entering the data into the project database. In a random sample of 100 records, the error rate for electronic collection was 1.7 percent (24 items of 1,400). The error rate for paper collection was 5.5 percent (77 items out of 1,400). This is a 69% reduction in errors when compared to traditional paper collection.

Larger and/or more complex data collection tasks would be expected to show greater time/cost efficiencies and increased error reduction rates in the collection and production of the data record. Although not all such data collection situations will raise the same challenges and each situation has its own unique characteristics and requirements, results to date have been very encouraging, and we are very excited about this use of PDAs. From our perspective, we believe that PDAs are a highly capable tool for field data collection. We are now beginning pilot testing of the use of PDAs for field data collection tasks by teachers in special education classrooms.

Conclusion

We believe that special education teachers and students would realize significant benefits from teachers’ using PDAs to collect data in the classroom. Classroom data collection would help teachers discharge their responsibilities under IDEA-97 and could provide teachers with information helpful to improving students’ academic achievement. PDAs, as the collection tool, make the process less cumbersome and more time and cost efficient. As teachers collect more data from their classrooms, they should begin to see the utility of the information as it pertains to teaching approaches, methods and results.

Table 1

Data Collection Measures

Measure Description
1. Number A simple count of the number of times that an event occurs used for behaviors that are brief or discrete
2. Percent The number of times an event occurs divided by the total number of opportunities multiplied by 100, used for behaviors that are brief or discrete
3. Rate Number of occurrences divided by the amount of time the behavior was monitored (e.g., seconds, minutes, hours)
4. Duration
a. Total
b. Per occurrence
a. The length of time that a single behavior occurs
b. The length of time that each behavior occurrence lasts in a series of similar behaviors
c. Latency
The elapsed time between the prompt and the occurrence of the behavior
d. Magnitude
The strength of a behavior

Kerr & Nelson, 1998

Table 2

Traditional Data Recording Strategies

Strategy Description
Permanent Product A tangible artifact of the behavior (e.g., student’s test or paper, video tape of behavior)
Event Recording A record of the number of times that a behavior occurred
Duration Recording A record of the length of a behavior occurrence
Response Latency A record of the time between a prompt and the occurrence of a behavior
Interval Recording At a set interval (e.g., every five minutes) it is recorded if the behavior is occurring or not

Kerr & Nelson, 1998

References

MSNBC. (2001, February 22). Palm Pilot captures Navy Hearts. [On-line].

Smith, D. (2001). Introduction to Special Education: Teaching in an Age of Opportunity (4th Ed). Allyn and Bacon: Boston, Massachusetts.

Walton, T. (1985). Educators’ response to methods of collecting, storing, and analyzing behavioral data. Journal of Special Education Technology, 7(2), 50-55.

Kerr, M. & Nelson, C. (1998). Strategies for Managing Behavior Problems in the Classroom (3rd Ed). Merrill: Upper Saddle River, New Jersey.


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