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High School Computer Science Education: A Five-State Study

Chris Stephenson
University of Waterloo

Abstract | Introduction | Methodology | Results | Conclusions | References | Acknowledgements | Contributor | Appendix A: Survey of Resources for Teaching Computer Programming

Results

Overall Response Rate

As Table 1 indicates, in all cases, the state-by-state response rate exceeds the traditional survey return rate of 5% by a considerable margin. It should be noted, however, that the 8.71% response rate from Massachusetts suggests that any conclusions drawn for this state alone should be considered less reliable than for states with higher return percentages.

Table 1: Response Rates
	Schools Surveyed		Schools Responding		Percent Responding
California	991		176		17.75
Massachusetts	539		47		8.71
New Hampshire	135		26		19.25
New Jersey	606		85		14.02
Washington	239		41		17.15
Total	2,510		375		14.94

Teaching Responsibilities

To determine the extent and mix of their teaching responsibilities, participants were asked to indicate whether they taught programming, computer applications, or other.

The results shown in Table 2, when compared on a state-by-state basis, indicate that a higher percentage of respondents considered themselves primarily computer application teachers as opposed to computer science teachers. The predominance of educators who identify themselves as computer application teachers may be related to the perception that computer application skills (i.e., learning to use the computer as a tool) should be the focus of computer education at the high school level. Though there is growing acknowledgment that all students require some level of competency with computers, there is less awareness of the importance of fundamental computer science concepts in the attainment of information fluency. This emphasis on teaching students how to use the computer as a tool also diminishes the likelihood that computer science will be part of the core (mandatory) curriculum at a given school.

Table 2: Teacher Responsibilities
	Programming		Applications		Both		Other
California	49.4		75.6		33.5		58.7
Massachusetts	67.1		72.3		38.3		52.1
New Hampshire	65.4		69.2		42.3		46.2
New Jersey	73.8		50.6		31.0		49.4
Washington	43.9		43.9		19.5		43.9
All states	57.7		67.7		32.6		53.5

We were somewhat surprised to discover that fewer teachers than expected reported teaching both computer science and applications, because we had assumed that the computer science teacher was likely to be the school "computer expert" and thus responsible for all curriculum areas relating to computers. Fewer than half of the respondents in any state reported teaching responsibilities for both computer science and computer applications, and more than half indicated that they held teaching responsibilities in other curriculum areas. This finding may indicate that many schools offer applications or computer science, but not both.

Current Hardware Use

All of the surveyed states report use of both IBM-compatible (PC) and Macintosh (Mac) computers. In all cases, however, schools indicated a distinct difference in both the number of labs and machines within those labs. As Table 3 shows, PCs are the dominant hardware platform in computer science education, both in terms of overall use in schools and the number of machines per school. Despite its earlier predominance in some states, the Mac maintains a persistent but much less prominent place. The average number of machines per lab also varies significantly from state to state, but all of the states report a greater number of PCs than Macs per lab.

Table 3: Current Hardware Use
	Average Number of PCs		Average Number of Macs		PC Lab Size		Mac Lab Size		Percent of Schools with PC Labs		Percent of Schools with Mac Labs
California	29.8		8.0		44.0		35.2		67.6		22.7
Massachusetts	25.1		2.7		31.9		12.9		78.7		21.2
New Hampshire	19.2		1.8		24.9		12.0		76.9		15.3
New Jersey	41.0		6.1		53.6		31.9		76.7		19.0
Washington	19.0		1.9		26.0		13.2		73.1		14.6
All states	29.8		5.8		41.3		28.6		72.2		21.3

The average number of computers of each hardware platform per school is less relevant, because high schools tend to concentrate computers in lab settings. These results, however, further demonstrates the preeminence of PCs. In New Jersey, for example, schools on average have 41.0 PCs and 6.1 Macs. The consistency of these figures across the states is demonstrated by the overall results, which show that schools on average use 29.8 PCs and 5.8 Macs to teach computer science.

Schools Purchasing New Machines

Fewer than half of all of the schools responding reported that they were intending to purchase new computers in the 2000–2001 school year. Massachusetts reported the highest percentage of schools planning to purchase new computers, with 48.9%. New Hampshire, with 30.0%, reported the lowest.

Table 4: New Computer Purchases
	Buying		Buying PCs		Buying Macs
California	43.2		89.5		18.4
Massachusetts	48.9		78.3		34.8
New Hampshire	30.8		62.5		37.5
New Jersey	40.0		88.2		17.6
Washington	46.3		84.2		15.8
All States	42.7		85.6		21.3

Further evidence of the PC's dominance can be seen in the reported purchasing intentions of the survey respondents. More than 80% of the schools planning to purchase new computers within the next school year indicate that they are planning to purchase PCs, compared to the approximately 20% planning to purchase Macs. This purchasing pattern is consistent with current use.

Although the researchers attempted to gather more detailed information concerning the numbers of machines each school was planning to purchase, the results proved unreliable, because most respondents indicated that they were unable to accurately predict numbers. Thus, these results are not reported.

Hardware Purchase Criteria

The researchers surmised that the criteria on which schools based their computer purchases would be quite broad. To provide respondents with the widest possible latitude, the survey did not include suggested responses. The data therefore consists entirely of educator-generated categories.

For the sake of clarification, it is important to note that the category "compatibility" refers to compatibility with existing types of hardware and compatibility with computer networks. Teachers also tended to differentiate between "durability" and "reliability," with the former indicating the ability of the hardware to stand up to student use over long periods of time and the latter referring to, as one teacher noted, "my feeling that it kept doing what it was expected to do on a day-to-day basis with no nasty little surprises."

Table 5: Criteria for New Computer Purchases
	California		Massachusetts		New Hampshire		New Jersey		Washington		Average
School board decision	6.8		6.4		0.0		2.4		4.9		5.1
Compatibility	18.8		27.7		19.2		14.1		14.4		18.4
Cost	24.4		25.5		34.6		28.2		31.7		26.9
Durability	7.4		0.0		0.0		7.1		0.0		5.1
Ease of use	8.0		6.4		7.7		0.0		7.3		5.9
Industry standard	2.3		6.4		3.8		3.5		9.8		4.0
Internet capabilities	2.3		2.1		0.0		0.0		2.4		1.6
Networkable	4.0		0.0		7.7		2.4		4.9		3.5
Manufacturer	1.7		0.0		0.0		1.2		0.0		1.1
Memory capacity	5.7		10.6		11.5		12.9		19.5		9.9
Reliability	15.9		19.1		38.5		9.4		17.1		16.5
Speed	8.5		14.9		15.4		14.1		24.4		12.8
Teacher decision	1.7		2.1		0.0		1.2		0.0		1.3
Technical support	6.3		0.0		3.8		2.4		0.0		3.7
Upgradeable	1.7		4.3		0.0		5.9		0.0		2.7
Warranty	6.3		0.0		0.0		3.5		0.0		3.7

Though there was some difference in emphasis from state to state, overall the reported criteria was fairly consistent. The cumulative responses from all states indicate that cost (26.9), compatibility (18.4), reliability (16.5), speed (12.8), and memory capacity (9.9) are the most commonly identified criteria for computer hardware selection.

The importance of cost as a criterion for hardware purchase came as no surprise to the researchers in light of the current environment of fiscal hardship in many schools and school districts.

The concern with compatibility may also be seen as a resource issue. Often, schools do not have easy access to computer technicians, so the responsibility for maintaining the hardware and networks often falls to the computer teacher. Ensuring compatibility with existing machines and networks reduces overall complexity by allowing teachers to concentrate on a single hardware platform. This emphasis on compatibility may also promote long-term stability in hardware use patterns, because schools are more likely to continuing using and purchasing the hardware platforms with which they are already comfortable.

Despite this apparent consistency of use and purchase, schools do not place similar emphasis on the hardware brand or manufacturer, thus platform loyalty has little or no connection to vendor loyalty. This, again, may reflect the high level of price sensitivity in the education market, which leads schools to search for what one teacher described as "the best bang anyone will give us for the buck." When teachers refer to the "bang," most often they are referring to the most frequently cited performance criteria: reliability, memory capacity, and speed. The researchers found it interesting that even in an academic setting, these criteria would be considered more important than pedagogical concerns such as ease of use.

The researchers were also somewhat surprised by the infrequent reference to hardware industry standards (what schools perceive as being most commonly used in business and industry), because industrial relevance is the most widely reported criterion for programming language selection. One possible explanation is that schools, with no hope of matching the frequent replacement cycle for computer hardware in industry, have simply abandoned this criterion as unattainable.

The relatively infrequent mention of Internet capabilities as a hardware selection criterion may result from a number of possible factors. Though an increasing percentage of schools are now reported to be wired (have Internet access), this capability is often limited to a small number of machines in the schools rather than the larger labs used for computer science instruction. In fact, in a traditional computer science lab setting, teachers may find such access more of a hindrance than a help because of the potential for students to be distracted from classroom tasks. Internet capabilities may also be perceived as less related to the computer science curriculum (which focuses on algorithm development and programming) than to the computer applications curriculum (which focuses on using the computer as a tool). One final possibility is that Internet capabilities are now so ubiquitous that educators no longer consider this a criterion requiring consideration.

Although the researchers did not attempt to establish a direct connection between hardware platforms actually used in the schools and the educator’s stated criteria for hardware purchases, this may prove fertile ground for future research, because the results of this survey indicate a fairly consistent hierarchy of educator concerns.

Teaching Programming in Grades 10, 11, and 12

Schools were asked to indicate all grades in which programming was taught. With the influence of the Advanced Placement exams at the higher grade levels, it was expected that more schools would indicate teaching programming in later rather than earlier grades. This was not, however, always the case.

Despite the widely reported career opportunities for computer professionals, the results shown in Table 6 indicate that fewer than 50 of schools teach computer science in Grades 10 and 12 and just slightly more than 50 do so in Grade 11. These results are particularly surprising in California, for example, because its Silicon Valley is world renowned as a site of both hardware and software innovation and employment opportunities.

Table 6: Programming in Grades 10–12
State	Grade 10		Grade 11		Grade 12
California	38.6		47.7		48.3
Massachusetts	44.7		55.3		57.4
New Hampshire	61.5		57.7		57.7
New Jersey	56.5		62.4		69.4
Washington	29.3		36.6		34.1
All states	44.0		51.5		48.0

The relatively low number of high schools offering computer science instruction may be related to a number of factors. Schools may perceive that, because of its resource requirements and/or academic rigor, computer science instruction is best left to the colleges and universities. Fisher, Margolis, and Miller (1997), for example, refer to frequently expressed student impressions of computer science, which include the prevalence of "really smart" students and an extremely heavy workload.

They also note that many young women still consider computer science a predominantly male domain. This preconception may discourage young women from taking computer science classes. This may in turn contribute to smaller class sizes, which may make it untenable for smaller schools to offer such courses.

The lack of qualified or interested teachers may also affect the education system’s ability to offer computer science courses, because the salaries and working conditions offered by most schools do not compete with those in business and industry, especially for individuals with skills that are in such high demand.

Programming Languages Taught in Grades 10, 11, and 12

Respondents were asked to indicate which programming languages they taught in each grade. (Because instruction often involves more than one programming language in each grade, totals often exceed 100%.) The following tables show the results for Grade 10, 11, and 12.

Tables 7, 8, and 9 show a pattern similar to that reported in earlier studies of colleges and universities (Stephenson & West, 1998). In both educational environments, there is a shift from structured programming languages such as Pascal and some versions of BASIC to object-oriented programming languages such as C++ and Java.

Table 7: Programming Languages in Grade 10
	California		Massachusetts		New Hampshire		New Jersey		Washington		Average
BASIC	26.5		0.0		18.8		52.1		0.0		27.9
C++	48.5		57.1		62.5		43.8		50.0		49.7
HTML	11.8		23.8		18.8		2.1		25.0		12.1
Java	5.9		0.0		12.5		10.4		0.0		6.7
JavaScript	0.0		4.8		6.3		0.0		8.3		1.8
Pascal	10.6		4.5		25.0		13.2		8.3		11.2
Perl	0.0		4.8		6.3		0.0		0.0		1.2
Visual Basic	17.6		42.9		31.3		20.8		50.0		24.2

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Copyright © 2002, ISTE (International Society for Technology in Education). All rights reserved.