Electronic Homework

 

 

Fong-lok Lee & Rex M. Heyworth

The Chinese University of Hong Kong

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Electronic Homework

Fong-lok Lee & Rex M. Heyworth

The Chinese University of Hong Kong

 

Homework, traditionally done with pencil and paper, can be an effective means of helping students to consolidate what they have learned.. When students have problems, some fortunate ones may have immediate assistance from their parents, siblings or others who act as human tutors. However, most of them have to wait until the next day before they can ask for their teachers’ help, so while they are doing their homework, they must resort to other means of overcoming any difficulty which, if unresolved, may become sources of later errors [1]. It would be desirable if a personal tutor could be made available to help each student when they encounter homework problems.

 

A Personal Tutor for Students

There are good reasons why a personal tutor should be able to help students of varied abilities. For better students, immediate feedback should be provided to strengthen the learning effect. For students of lower ability, misconceptions or errors should be corrected immediately to prevent them becoming stable errors [1]. If and when students do not know how to continue, immediate help should be provided so that students' correct behavior will be reinforced and incorrect behavior avoided. Ideally, this should be done by human tutors, but if human tutors are not available, computer tutors that can act like human tutors may be an alternative solution.

To provide a computer tutor to each student is easier said than done. The necessary condition is that each student should have a personal computer at home, a condition which is not fully met at the present time. However, with the increasing prosperity of society and the lowering of the price of personal computers, it is possible that in the coming few years, this condition will be satisfied.

The biggest handicap is the lack of suitable software, particularly in the area of mathematics. Most currently available computer-assisted instructional systems in mathematics focus on tutorial and drill-and-practice functions [2]. Students are given exercises to practice, either in the form of multiple choice, or short questions for which only short answers are expected. The purpose of this kind of software is to drill the students until they reach a certain degree of competence which is measured by a test similar to the exercises. Those who pass the test are allowed to go on to the next part of the system whilest students who cannot pass the test will be asked to either revise certain parts of the material or be given additional materials to read until they master the subject. There is no attempt to understand individual students' errors and all remedial measures are prespecified.

 

What a Homework System Should Do

Recently, computer systems have begun to attempt to understand students’ errors, and then provide assistance to help students overcome them. Examples can be found in the area of linear algebraic equations in one variable [3][4][5], geometry [6] and calculus [7]. However, to be a true homework assistant system, the following conditions have to be satisfied in addition to providing tutoring to students:

    1. Teachers or even students should be allowed to enter problems that they would like to have as homework. To achieve this, the computer software has to be able to find the solution to any problem entered as well as detect any errors made by the student during the problem solving process.
    2. Besides helping students, the system should help teachers by scoring students’ work. Furthermore, it should help teachers to better understand their students by collecting and summarizing their errors and weaknesses shown in the students’ work. This frees the teacher from much tedious routine work, but still allows them to quickly ascertain the students’ progress.

Our Electronic Homework designed to achieve this purpose is composed of two components: the Computer Tutor and the Homework Administrator. The Computer Tutor is an intelligent tutoring system that can provide personal assistance like supplying hints, checking errors and providing remediation. The Homework Administrator is a teacher’s assistant which assigns and marks the homework, summarizing errors for the teacher’s reference and prioritizing problems. When using Electronic Homework, teachers would simply assign homework by distributing floppy disks containing the assignment for students to do at home. Students can work at their own pace under the guidance of the computer tutor, and next day when they return the disks to school, teachers do not have to mark or correct the homework because it has already been done by Electronic Homework. However, teachers will, after collecting the disks, have a clear picture of how the work was done by having the computer summarize students’ progress and displaying it on the screen. Teachers therefore have more time to focus attention on improving their teaching.

 

System Design

In order that Electronic Homework can achieve the above purposes, both the components -- the Computer Tutor and the Homework Administrator – are themselves composed of several modules. The following sections describe the content and function of these modules.

 

Knowledge Stored in the Computer Tutor

So that the Computer Tutor can diagnose students’ errors as well as describe them, different types of knowledge, mainly obtained from human experts, has to be incorporated. To allow for future expansion, the pieces of knowledge, in the form of rules, are stored in separate modules. Each module is described in more details in the following paragraphs.

 

The Expert Module

This contains the knowledge that the system imparts to the student. It is called an expert module since it includes what an expert in the subject area concerned should know. As the present system is intended to teach logarithmic knowledge, it comprises mainly knowledge required to solve logarithm problems. However, solving logarithm problems may require other mathematics knowledge such as solving algebraic equations, simplifying algebraic expressions and factorizing numbers or algebraic expressions. The knowledge base therefore includes a large number of rules.

In Electronic Homework, there are two types of rules: the strategic rules and the axiomatic rules, as suggested by Lewis, Milson, & Anderson [5]. Strategic rules state what strategies would be used whenever certain patterns are observed, while axiomatic rules correspond to behaviors according to mathematics axioms. The following examples found in The Teacher's Apprentice [5] serve to illustrate this difference:

[R1]

IF

the equation to be solved contains a subexpression of the form num(term1 + term2)

THEN

set as a subgoal to distribute num over term1 and term2

[R2]

IF

the goal is to distribute num over term1 and term2

THEN

set the subgoal to multiply num times term1

AND

set the subgoal to multiply num times term2

AND

set the subgoal to combine the previous results with +

[R3]

IF

the goal is to multiply num times term

THEN

write the product of num and term

[R4]

IF

the goal is to combine term1 and term2 with a +

THEN

write term1 + term2

(Words in italics are variables.)

In the above examples, the rule [R1] recognizes that distribution is applicable to the equation and sets the subgoal to distribute num over term1 and term2. It is a strategic rule. The other three are axiomatic rules since they show the actions according to distributive law, multiplication and addition facts respectively.

Strategic rules and axiomatic rules, together, form the domain knowledge base of the system. However, according to Roberts & Park [8], besides this domain knowledge base, there should be one more element which they call the criterion-performance model. The domain knowledge base includes both the knowledge of the contents to be taught and the knowledge on how to use the content knowledge to solve related problems. The criterion-performance model is a computer-based expert that solves the same problem which the student is working on so that the system can evaluate the student’s performance.

 

Criterion-performance model Versus Model-tracing

There has been some argument as to whether the criterion-performance model is necessary for a computer tutor. An example of systems employing this model is PIXIE [3][4]. In this system, the solutions to a problem, whether correct or incorrect, are generated before the problem is presented to the students. Students' answers are then compared with these generated solutions as models and instructions will be presented in response to those answers found identical to an incorrect model.

In systems like The Teacher’s Apprentice [5] and LISPITS [9], there is no specific criterion-performance model. Instead, they use a "model tracing" method of tutoring. At each state (step) of the process, the system infers the learner’s internal state by matching the learner’s output with the problem state generated by using ideal and incorrect rules (referred to as buggy rules). Instructions will be given according to this inference.

Both the model-tracing and the criterion-performance model have their advantages and disadvantages. The criterion-performance model approach usually requires a lot of space to store the models and also a heavy investment of time to generate all the possible models while the model-tracing approach might prevent the student from learning by making errors because it disallows the students from exploring other possible solutions. A detailed discussion on whether the criterion-performance model or model tracing method should be employed in the present study is presented later in the sections on tutoring module.

 

The Student Module

The function of a student module is to store knowledge which might be possessed by students, both correct or incorrect, in the form of rules. Traditionally, student modeling falls into two broad categories: the quantitative method [10], which is mostly used in conventional computer-based instruction (CBI) which will not be elaborated further here, and the qualitative method. Clancey [11], in defining qualitative models, says “The qualitative model is neither numeric nor physical analogues. Rather, it describes objects and processes in terms of spatial, temporal, and causal relations.” There have been mainly two types of qualitative methods used to model students as follows:

1. Overlay model: the student’s performance is compared to that of the computer expert. "The expert’s competence is assumed to be broken into a set of skills so small that the pupil either has them or doesn’t" [12]. In other words, the student has some part of the expert’s knowledge.

2. Bug identification method: the student model contains both domain knowledge as rules and misconceptions, and errors (bugs) as variants of rules. In this case, the student model includes something that the expert does not have but the student does. It is thought to be more realistic than the first type.

Figure 1 shows the relationship of the student model to the expert’s behavior and the bugs [12].

-- insert Figure 1 here --

 

 

This system is designed to help students to correct their errors and because the overlay model does not contain such knowledge, it is reasonable to say that the bug-identification model would be more appropriate for the present use. The student module in the present system therefore incorporates mal-rules, in addition to the correct rules. The mal-rules simulate students’ errors obtained from a set of tests, called mal-rule collection tests, administered to 125 secondary 4 (grade 9) students in Hong Kong.

 

The Tutoring Module

This module mainly contains the knowledge on how and when to help students to correct their errors. This is based firstly on evidence has been given by Lee [13] that producing conceptual dissonance (i.e., showing the students that their errors are contradictory to their previous knowledge) in the students’ mind, and having students practising the correct rules, can help them to correct their errors more effectively than methods like reteaching and model-based remediation [14]. The prescriptive rules used in Electronic Homework are based on this principle.

The second basis for when to remedy students’ errors, relates to the argument about which of the above models, the criterion-performance model or the model-tracing method, should be used. The main differences between the two methods, is the amount of freedom to explore, given to students during the interaction process, and the possibility of forming mal-rules due to incomplete learning. In the first mentioned, instructions are only given at the end of each problem, thus allowing students to flounder freely. In the process of floundering, students may make errors but it is also possible that they would discover their errors and correct them. Floundering may be good experience for those students who are able to correct errors since it helps them remember the correct rule. However, for those who cannot discover their own errors, floundering can become a source or future errors since the errors made are not corrected immediately [1]. The Model-tracing method keeps the students away from making errors, but at the same time, may force them only to memorize the correct rules without really understanding them. Clearly, both methods have their advantages and disadvantages.

For practical considerations, however, the model-tracing method has the advantage that there is no need to store a large number of problem solving models, which makes it easier for a computer system to handle. The model-tracing method is therefore employed in the present system.

 

The Communication Module

This module deals with the interaction between the human and the computer tutor. Normally, the module’s work consists of translating the human language into computer language and translating computer language into that which humans can understand, the input and output components of the system respectively. The translating of human language is not an easy task since human language is not well defined and is sometimes even illogical. To tackle this problem, some intelligent systems such as Meno Tutor [15] use a kind of restricted language in which the vocabulary consists only of a limited number of terms and the grammar used is strictly defined. With this kind of language, the computer can understand what the human user enters and react suitably.

Another type of system solves the language problem by displaying icons on the screen so that the users can choose their actions by simply clicking the appropriate icon by using the mouse. An example is The Teacher's Apprentice [5]. As only a limited number of icons can be displayed on the screen, the latter method is only capable of handling simple interactions. However, even the former limited-language method cannot allow complex human-computer interactions. It only works well in restricted domains such as mathematics or computer programming.

As it is believed that the displaying of icons would remind students on how to solve the problem, the iconic approach is not adopted since such iconic display is not normally found in students’ exercisess. Also, Electronic Homework is intended to be used by school students who may not be good at typing. These students will find it easier to use a mouse to input information. All the symbols required in a logarithmic expression are therefore displayed on the screen so that students can simply use the mouse to click those they wish to put in their own expression, thus reducing the troublesome task of typing in the expressions in full. In addition, this also reduces their chances of making low-level errors such as missing brackets.

To reduce a student’s working memory load, the required formula as well as other given values are displayed on the screen for easy access. Further, the screen is divided into three parts: one for displaying each step the student takes in solving the problem, another for displaying feedback to the student and the third for displaying formula and constants. In this way, the student can immediately know where to concentrate during the problem solving process.

 

The Homework Administrator

The Homework Administrator is responsible for the handling of routines that are normally done by teachers which include the sequencing of problems, collecting students’ errors and the marking of students’ work. The following paragraphs describe how these tasks are done.

 

How to Order Problems According to Their Difficulty

Evidence has shown that arranging problems in terms of their difficulty benefits students [16]. Although item difficulty is usually measured by the item difficulty ratio which is the ratio of the number of students who answer the item correctly to the total number of students making the attempt. As this can only be determined after the item is administered, it is impossible to use it for the present purpose. The present system uses a measure called ‘Problem Complexity’ [17] to sequence the problems. The formula for calculating Problem Complexity is as follows:

Problem Complexity = 0.11 Machstep + 0.19 Notmfac +

.17 Familar + 1.17

where Machstep is the number of steps required by the computer system to solve the problem, Notmfac is the number of operators (+, -, *, /, etc.) in the problem expression and Familiar is the degree of familiarity of the problem to the students (a value of “1” will be given to problems that require students to simplify expressions involving logarithms of numbers, “2” for problems require simplifying expressions involving logarithms of variables and “3” for solving logarithmic equations).

The difficulty of each problem can be calculated by the system using this formula, and problems entered by teachers can be arranged accordingly. Teachers therefore do not have to take care of the ordering of problems.

 

How StudentsErrors Are Collected

As students are doing their homework on the computer, every error made is recorded, whether the error is finally corrected or not. The list of errors is then stored on the floppy disk to be submitted to the human teacher. When all the disks are collected, both the list of errors admitted by an individual student and a summary of errors admitted by all the students can be displayed on the computer monitor and the teacher can then very quickly tell how well the students have learned.

 

How StudentsWork Is Scored

Whether a student can correctly solve a problem or not can be easily checked by comparing the student’s answer with the machine generated one. Hence a student’s score in doing an exercise can be obtained by just counting the number of problems he or she correctly finished. This score is again stored on the floppy disk submitted to the teacher. When the disks are collected, the teacher can quickly know the performance of the whole class by having the computer display a summary of their scores.

 

The Tutoring Process – An Example

The tutoring process in Electronic Homework by one of the student records as follows: A problem was given and the student (a girl) was asked to solve the problem step by step. Figure 2 depicts what the screen shows when she was solving the problem.

- Insert Figure 2 about here –

Figure 2 shows what happened when a student tried to simplify the expression but mistakenly expressed it as . The computer had already prompted the student to re-enter the first time the error was made and the message on the screen was generated by the computer when she made the error again. The purpose of this computer-generated message is to induce a conceptual dissonance. The original message was in Chinese and can be translated as follows.

This is wrong. For example, does not equal to , since log 2 does not mean “log times 2”. Also does not equal to . Would you like to check whether the two quantities are really un-equal?

The computer tried to point out the student’s error and then led her to see what she was doing was in fact contradictory to what she believed. Since equals and equals , and the student should know does not equal , this forced her to realize her error and then fine tuned her own knowledge.

The same steps were repeated until the student finished the problem. Since the student had made errors twice in solving that particular problem, the system automatically generated three more problems for the student to solve. The problems were basically similar to the original problem and were only differed in numerical values. This method of generating conceptual dissonance and repeating three more problems was based on a method to help students correcting errors suggested by Lee [13].

 

Effects of Using Electronic Homework

After the system was developed, it was trialled in six schools. Six classes of Secondary 3 students in Hong Kong with wide range of academic abilities were invited by convenient sampling to participate in the experiment. A total of 220 students were required to attempt two homework sessions: Homework 1 and Homework 2, both are on the simplifying of logarithmic expressions and the solution of logarithmic equations in one variable. While Homework 1 consists of problems like easier and more concrete problems, Homework 2 consists of problems that require the knowledge to solve problems in Homework 1 and are considered as more abstract. Such categorization of problems was intended to investigate whether Electronic Homework can be better in helping one of these two types of problems.

Ninety-two of the students took Homework 1 with Electronic Homework and Homework 2 with pencil and paper. Another 98 students took Homework 1 with pencil and paper and Homework 2 with Electronic Homework. Electronic Homework requires at least a 80486 personal computer but in two schools there were not enough 80486 computers to go around. Therefore the remaining 30 students had to take both Homework 1 and Homework 2 with paper and pencil. Although this was not in the initial plan, this arrangement served a useful purpose. By comparing these 30 students with those using computers to do their homework, the effect of using Electronic Homework could be tested.

The results of these students’ homework completed either with Electronic Homework or paper and pencil, were recorded. Also, the learning results of doing homework, either by Electronic Homework or by conventional paper and pencil, were measured by a retention test consisting of items with difficulty levels similar to the homework. The retention tests were administered to the students approximately one week after the homework sessions. Together with the retention test, each student was asked to complete a Learning Processes Questionnaire [18] which was designed to categorize students as deep or surface learners. According to Biggs [18], “learning approaches refer to predispositions to adopt particular processes, i.e. how the students usually go about learning”. When solving problems, a surface learner involves with the “reproduction of sufficient detail to meet demands minimally”, while a deeper learner tries to “understand and come to grips with the heart of the problem”. Lastly, students’ academic abilities were calculated by using the following formula:

where the mathematics scores were students' results in a recent examination or test as supplied by the respective schools, and the homework score was the score obtained in doing conventional homework. Since students came from different schools and thus might be of different standards, the use of this formula was to ensure that this score reflected their abilities. Students’s ability scores were then used to categorize students into three academic ability types: high, median and low, for later analyses.

Overall Effect on Using Electronic Homework

The effects of using Electronic Homework were investigated by analyzing the retention test scores. The retention test consisted of two parts, referred to as part 1 and part 2 respectively. While items in part 1 are made equivalent to those which appeared in Homework 1, items in part 2 are equivalent to that in Homework 2. None of these scores were found significantly different between the group using Electronic Homework in Homework 1 then paper-and-pencil method in Homework 2 and the group using paper-and-pencil method in Homework 1 then Electronic Homework in Homework 2.

Comparison between students who used Electronic Homework and those who did not

The above comparison was done between groups using Electronic Homework in Homework 1 and in Homework 2. It may be desired to study the effect of the system on students who used Electronic Homework and on those who did not used it. The fact that 30 students from two of the participating schools did not have the chance to use Electronic Homework provided a chance to do so. The results of these students were compared with other students who used Electronic Homework in these two schools, since it would be inappropriate to compare their results with students in other schools who may have higher academic abilities and different school environments. Again, no significant difference could be found.

Since this analysis was carried out only with students in two schools (schcode=5 and schcode=6), and, it happened that these were students of relatively low academic ability, it can still be argued that with students in other academic ability groups or other schools, the effect might be different. The following findings reveal such a possibility.

Insert Table 1 about here

Effects on Individual Schools

 

Insert Table 2 about here

 

Table 2 shows the results of analysis of variance for the total retention test score. Although the main effect attributed to Homework type was not significant, there were main effects which could be attributed to the two variables Schcode and Acadtp, which meant that students with different academic abilities or who belonged to different schools would have different retention scores. The effect of academic ability on the test scores would be generally agreed but the effect of school was not understood. It is possible that some unobserved factors that favour the use of Electronic Homework can be revealed by studying these schools individually.

School Effect

Retention test scores of individual schools were analysed by using analysis of variance. In doing these analyses, two modifications were made in order to avoid statistical difficulties caused by empty cells. The first modification was due to fact that only 13 students could be categorized as using the deep learning approach. When these students were further divided into the different cells when two additional variables Academic Ability Type and Homework Type were added, some of these cells became empty and thus caused difficulties in the analyzing process. The variable Learning Style was thus excluded in later analyses. Reasons why there were too few deep approach students might be interesting but are not be the focus of the present study.

The second modification was caused by the analysis to the students in a particular school (Schcode=2). The retention test scores of students in this school were so high that only two students could be found in the low academic ability group which then led to an empty cell in the analysis. For this school, the analysis was done for the mediate and high academic ability groups only.

Of the six participated schools, significant results can only be found in the school (Schcode=2) with the highest Mean Retention Test Scores. Table 3 shows the results of analysis of variance for retention test scores for this school.

Insert Table 3 about here

Table 3 shows that students’ academic abilities (Acadtp) significantly affected all three scores in the retention test but the Homework Type (Hwrktp) affected only the scores students obtained in the first part of the retention test (Rtp1). As Rtp1 measures problems in the first part of the retention test were considered as less abstract than that in the second part of the test. This result suggests that the use of Electronic Homework might help student on easier problems.

Who benefited more and who benefited less

No significant effect could be found for the use of Electronic Homework in general. However, when the school with the highest academic ability was studied, it was found that Electronic Homework can help students to learn less abstract problems better. It is possible that only those students with high academic abilities would learn better when the system is used in less abstract problems.

Exactly what caused the difference is not known, but there can be two possible reasons that make the students in the school that Electronic Homework has effects distinct from the others. The first one is that most of the students of this school came from higher socio-economic families. The second one, as observed by the research, is that the students were highly motivated. Whether this socio-economic background or motivation can be the factors affecting the effect of using Electronic Homework has to be further investigated. Our research question may be, not on whether students can benefit from using the system, but on the identifying which types can benefit from using it.

Conclusion and Discussion

A major shortcoming of Electronic Homework, revealed by a questionnaire completed by the students, is that it reacts very slowly in some situations, especially when there is no corresponding mal-rule available in the system. This slowness is due to the fact that the system has to scan over its large base of stored rules before it can give out a “diagnosed error”. There is quite a large number of possible combinations of rules. One solution to this, is to use faster machines, which may become available within a few years. A further improvement may come from examining the fact that even though a computer can process much faster than human beings, in many situations, humans seem able to find an answer while the computer is still searching for possible solutions. It is thus not just the processing speed, but the strategies used, which determine how fast a reaction is. If human strategies can be better understood and can be incorporated in the system, a much faster system can be constructed.

Another possible shortcoming of the system is the input methods it allows. Currently, students can use either the keyboard or the mouse pointer to input a mathematical expression. Yet the expression displayed can only be in the form of a single text expression. An example of this is that “one half” can only be displayed as “1/2”, not in the usual way of 1 over 2. Precisely how this would influence the effect of using the system has yet to be investigated, but it seems that the system might be much improved if some other input techniques like the use of a stylus tablet or even speech can be incorporated into the system.

 

It would be inappropriate to test a computer system in one or two periods. In additional to what described in the above paragraphs that students need to adapt to a new input method and to wait for the computer’s responses, they actually need to switch to a completely new working environment which they never experience. Hence besides that the system need to be improved in the future, the testing of the system should be done in a longer period so that the effect of using the system can be truly revealed.

References

[1] VanLehn, K. (1990). Mind bugs: The origins of procedural misconceptions. London: MIT Press.

[2] White, J. A., & Purdom D. M. (1996). Viewing modern instructional technology through conceptions of curriculum. Educational technology review, 6, pp. 5-9.

[3] Sleeman, D. H. (1987). PIXIE: A shell for developing intelligent tutoring systems. In Lawler, R. W. & Yazdani, M. (Eds). Artificial intelligence and education, 1. Norwood, NJ: Ablex.

[4] Moore, J. L., & Sleeman, D. (1988). Enhancing PIXIE's tutoring capabilities. International journal of man-machine studies, 28. pp. 605-623.

[5]Lewis, M. W., Milson, R., & Anderson J. R. (1987). The teacher's apprentice: Designing an Intelligent authoring system for high school mathematics. In Kearsley, G. (Eds). Artificial intelligence and instruction: Applications and Methods. Addison-Wesley.

[6] Anderson, J. R., Boyle, C. E., & Yost, G. (1985). The geometry tutor. Proceedings of the International Joint Conference on Artificial Intelligence. Los Angeles.

[7] Mao, Y & Lin, J. (1992). Intelligent tutoring system for symbolic calculating. In Frasson, C., Gauthier, G. & McCalla, G. I. (Eds), Proceedings, Intelligent tutoring systems, Second international conference, ITS '92. Springer-Verlag.

[8] Roberts, F.C., & Park, O. (1991). Intelligent computer-assisted instruction: An explanation and overview. IN The Educational Technology Anthology Series: Expert Systems and Intelligent Computer-Aided Instruction. New Jersey: Eaglewood Cliffs.

[9] Corbett, A. T., & Anderson, J. R. (1992). LISP intelligent tutoring system: Research in skill acquisition. In Larkin, J. H. & Chabay, R. W. (Eds). Computer-assisted instruction and intelligent tutoring systems: Shared goals and complementary approaches. Hillsdale, NJ: Lawrence Erlbaum Associates.

[10] Park O., & Seidel R.J. (1991). Conventional CBI versus intelligent CAI: suggestions for the development of future system. In The Educational Technology Anthology Series: Expert Systems and Intelligent Computer-Aided Instruction. New Jersey; Eaglewood Cliffs.

[11] Clancey, W.J. (1988). The role of qualitative models in instruction. In J. Self (Eds), Artificial intelligence and human learning. Chapman and Hall.

[12] Elsom-cook, M. (1988). Guided discovery tutoring and bounded user modeling. In J. Self (Eds), Artificial intelligence and human learning. Chapman and Hall.

[13] Lee, F. L. (1995). The rule of conceptual knowledge in remediation of procedural errors. Educational Journal, CUHK, 23(1). pp. 163-179.

[14] Sleeman, D. H., Kelly, A. E., Martinak, R., Ward, R. D., & Moore, J. L. (1989). Studies on diagnosis and remediation with high school algebra students. Cognitive science 13, pp. 551-568.

[15] Woolf, B. P. (1987). Theoretical frontiers in building a machine tutor. In G. Kearsley (Eds), Artificial intelligence and instruction, applications and methods. Addision-Wesley.

[16] Newman, D. L., Kundert, D. K., Lane, D. S., & Bull, K. S. (1988). Effect of varying item order on multiple-choice test scores: Importance of statistical and cognitive difficulty. Applied Measurement in education, 1(1), pp. 89-97.

[17] Lee, F. L., & Heyworth, R. M. (1999). Problem Complexity -- A Measure of Problem Difficulty in Algebra. Education Journal. Vol. 28, No. 1.

[18] Biggs, J. B. (1992). Why and how do Hong Kong students learn? Using the learning and study process questionnaires. Education papers. Faculty of Education. University of Hong Kong.

 

 

 

 

Figure 1 Two types of student models (Elsom-cook, 1988)

 

 

Figure 2 Computer Screening Showing A Student makes an error when simplifying an Expression

 

Table 1

 

Table showing the Mean Retention Test Scores of the Participated Schools

 

Rtp1

Rtp2

Rttot

Schcode

Mean

SD

Cases

Mean

SD

Cases

Mean

SD

Cases

1

7.62

3.03

35

3.69

3.15

35

11.31

5.23

35

2

10.56

1.68

36

7.22

2.23

36

17.78

3.51

36

3

8.82

3.80

29

4.86

3.36

29

13.69

6.60

29

4

7.81

3.00

36

4.53

3.52

36

12.33

6.12

36

5

5.83

3.34

36

3.17

3.22

36

9.00

6.04

36

6

6.89

2.73

28

2.57

2.12

28

9.46

4.20

28

Overall

7.94

3.31

200

3.40

3.32

200

12.33

6.11

200

Note. Rtp1= score in part one of retention; Rtp2=score in part two of retention test, Rttot=Total score in retention test.

 

 

Table 2

 

Analysis of Variance for the Retention Test Scores (Rttot) (N=220)

Source

Sum of Squares

DF

F

Schcode

1630.55

5

13.13***

Ltype

35.93

2

.72

Acadtp

719.84

2

14.50***

Hwrktp

9.01

1

.37

 

Note. Rttot=Total retention test score; Hwrktp = Homework type; Acadtp = Academic type; Ltype = Learning approach type.

* p< .05. ** p<.01. ***p<.001

 

Table 3

 

Analysis of Variance for the retention test score with Sch=2 (N=35).

Source

Rtp1

Rtp2

Rttot

DF

SS

F

DF

SS

F

DF

SS

F

Hwrktp (H)

1

6.82

4.10*

1

1.34

.566

1

2.11

.26

Acadtp (A)

1

15.08

9.07**

1

37.10

9.31**

1

99.48

12.34***

H X A

1

7.46

4.49*

1

.11

-03

1

9.41

1.17

Error

31

51.54

 

31

123.52

 

31

249.96

 

Total

34

85.89

 

34

163.54

 

34

382.97

 

 

Note. Hwrktp = Homework type; Acadtp = Academic type; Sch = School; Ltype = Learning approach type.

* p< .05. ** p<.01. ***p<.001

 

 

Appendix 1: Remedial rules

  1. 依你看: (1+2)*3,跟 1+(2*3) 有什麼不同? 如二者不同的話, 1+2*3 應取那一個值呢?
  2. 請留意這式跟上述二者唯一不同的地方,是有沒有括號及括號的位置. (等候學生回答)

    習慣上, 我們是先乘除後加減的. 即是說 1+2*3 = 1 + (2*3).

  3. 因 為 我們的答案的形式是 x=____
  4. 如果不將所有沒x的項移往右邊,而左邊只保留有x的項, 我們怎能得到答案呢?

  5. 將 +3 移往右邊, 要變成 -3 (-3 變成 +3, *3 變 /3, /3 變 *3), 如果不這樣做的話. 我們看看會怎樣:
  6. x + 3 = 5 變成 x = 5 + 3

    前式中的x 只要是2 便可以了, 但後式中的x 則變成 8 , 所以這是錯誤的.

  7. 例如說: 1+2=3, 這是對的, 但如果我們只在左邊加 4, 即是說:
  8. 1+2+4= 3, 這對不對呢?

    (可改為 1+2-4 = 3, 1+2 /4 = 3, 1+2 *4 = 3)

  9. 例如說: 2*(3+4) 應該是2*7 = 14, 而2*3+2*4=6+8=14, 所以2*(3+4)=2*3+2*4. 如果好像你剛才那樣做,....(模仿學生的做法,但以數字代入)...., 左邊便不等於右邊.