Spelling Problems Case Study

20 Dec Case Study: Beating Dyslexia

Posted at 06:31h in Overcoming Learning Disabilities: Top Success Stories by Susan du Plessis

Terry Andre had had remedial lessons for three years for his reading problem. “I have taught him to cope with the work but I have not been able to teach him to read. His reading has always been a serious problem and he has been diagnosed as dyslexic,” wrote Jennie van Zyl, Terry Andre’s remedial teacher.

In the mid-year of fourth grade Terry Andre’s reading ability was, according to Mrs. van Zyl, like that of a first-grade/second-grade child. His spelling was poor; he confused b’s and d’s, and found creative writing a problem. According to his class teacher, Mrs. Leslie, he was very untidy, always in a hurry, happy with second best and just generally not coping at all. Because the children laughed at his efforts, reading in front of the class caused him great embarrassment.

Terry Andre started on Edublox in the third term of fourth grade, a few weeks before the examination. He and Mrs. van Zyl spent half an hour, five days per week, on the program. He nevertheless failed third term with an aggregate of 54 percent.

Rapid improvement was noted during the fourth term. By the end of the school year Terry Andre was no longer reversing letters. “The other day,” Mrs. van Zyl reported, “he picked up a book and I was amazed to hear him actually reading without any stammering, hesitation or repetitions. It was the first time he had ever managed without being taught the words first. Now, whenever there is any spare time, he asks if we can read.”

His class teacher reported, “He has settled down, completes all his tasks to the best of his ability and takes pride in his work. He is also able to read aloud in from of the class without any of the problems which had caused him such embarrassment before.”

Terry Andre passed fourth grade with an aggregate of 66 percent.

Three months later Mrs. van Zyl sent in a report once more: “It gives me great joy to listen to him and see his incredible progress. Thank you for your program. It certainly brings results.”


An example of Terry Andre’s schoolwork just before he started on Edublox:

The striking improvement in Terry Andre’s work after only six months:

 


About Edublox Online Tutor

Edublox Online Tutor offers multisensory cognitive training that enables learners to overcome reading problems and learning challenges and reach their full potential. Over the last 30+ years, the company behind the Online Tutor e-learning platform, Edublox, has helped thousands of children to read, learn and achieve through home kits and learning clinics internationally. Our programs are founded on pedagogical research and more than three decades of experience demonstrating that weak underlying cognitive skills account for the majority of learning difficulties. Specific brain-training exercises can strengthen these weaknesses leading to increased performance in reading, spelling, writing, math and learning.

 


Science and Technology Education Center


Tel-Aviv University - School of Education



Knowledge Technology Laboratory

 

 

 

 

 

Tel Aviv University

School of Education

Ramat-Aviv, Tel-Aviv 69978

ISRAEL

 

 

 

 


[© by the journals or books publishers]

 


 

The acquisition process of correct spelling by low-vision children is affected by factors which can be referred to as cognitive consequences of their physical impairment. Visual information is crucial for the development of correct spelling. Ehri (1980) suggests that a word stored in the long-term memory holds a number of identities, i.e., acoustic, visual, syntactic, semantic, contextual, kinesthetic. The compound of all these identities constitutes the linguistic identity of the stored word. Since visual information plays a central role in the consolidation of linguistic identity, the lessened performance of the visual channel affects negatively the construction process of the syntactical properties of the stored word and consequently, the stored word's value as referential model for correct spelling (Ehri, 1980; Wohl, 1984; Perfetti, 1991).

When words have low-quality referential models this crucially affects the acquisition process of reading and writing skills. Low-vision children learn to read complete configurational units (e.g. words), and whatever the visual channel gets in incomplete or unclear form, the mind completes by using additional external (e.g., acoustic, contextual) and internal (e.g., a previously stored word-model) information. Low-vision children acquire writing by a similar "completion strategy", and the resulting combination of incomplete input information and often non-reliable, previously stored information, appears to be a frequent source of spelling mistakes (Mak, 1993).

It would be reasonable to expect that appropriate instruction could support low-vision children in improving the quality of their syntactic knowledge, and consequently improve their spelling. Such instruction, however, is not easily planned and implemented for two reasons. First, there is great heterogeneity in vision capability and spelling knowledge among low-vision children, requiring individually tailored remedial work or individual tutoring. Needless to say, practical and economical considerations put extensive implementation of regular one-to-one teaching out of reach for the vast majority of educational institutions. Second, teachers perceive individualized remedial tutoring as a complicatedprocess (e.g., involving complex diagnostic tasks, time-consuming planning, difficult implementation) whose functional profitability is uncertain. As a result teachers prefer to focus mainly on the content of the writing, and pay less attention to spelling. Minimal time is devoted to spelling remedial treatment, and curricular materials for spelling instruction (in Hebrew) targeted to this particular population do not exist.

The work reported in this paper is an attempt to support low-vision children in coping with spelling mistakes, by means of a diagnostic and remedial adaptive computer tool. Our work is guided by three main assumptions: (a) low-vision children's impairment in the visual channel should be compensated by alternative information-input channels; (b) the support to be given should build upon the children's spontaneously activated "completion strategy" (the functional merging of information from varied sources); and (c) the support should be highly individualized, given the extraordinarydifferences in vision and spelling ability among the target population.

Based on these assumptions we developed "Pupil", a computer adaptive remedial system. This paper reports on a comprehensive case-study which evaluated the effect of the computer-based remedial process on a low-vision child's spelling performance. Our research questions were:

(a) Does the remedial plan affect the overall spelling performance?

(b) Does the remedial plan support automation of spelling?

(c) What characterized the remedial process in terms of:

(i) the stabilization process of misspelled words?

(ii) the patterns of use of support tools?

(iii) the patterns of use of the visual and auditory channels?


A full and complete description of the computer system is beyond the scope of this paper (see Lahav, 1997). In the following paragraphs however we offer a succinct presentation of features that are necessary for the understanding of the procedure and results of the study reported inthis paper.

The "Pupil" system diagnoses misspelling by categories of mistakes and by spelling rules. One main component of the system is the diagnostic module, and the other is the remedial module. The unit of work in "Pupil" is the task, a set of 15 exercises composed by the system on the basis of diagnostic data. A typical exercise presents a sentence in which the target word is missing, and it has to be typed in by the student.

The catalog of words and sentences from which the exercises are composed was the result of a previous study which mapped the low-vision students' most frequent spelling mistakes in the Hebrew language (Lahav, 1997). As a result we identified three categories of mistakes: letter-shift (e.g., between the Hebrew characters ) due phonetical similarity; omission of letters; and unnecessary addition of letters. The resulting catalog of 523 words was indexed according to two criteria: the syntactic rule and degree of difficulty (and estimation of appropriate grade level) of each word. The indexing and classification was made by three independent experts in language teaching.

At the beginning of each session student have the possibility to adjust basic layout features (e.g., font size, font and background color, sound) to their needs and preferences.

The first session with "Pupil" is a diagnostic session, as a result of which the subject's spelling difficulties are modeled and located within a space of grammatical and intuitive rules (Lahav, 1997). Following come the treatment sessions, during which the student model (a representation of the student's state of knowledge and mastery of spelling rules) is continuously updated. This model guides the generation of the subsequent tasks by the system. For example, words or rules identified as stable are eliminated from the list of candidate-components for the generation of new exercises, new misspelled words and rules are added to this list, or exercises for words or rules in unstable status are generated. In a typical sessionUa target word (corresponding to a target rule) appears several times every three exercises. Every correct answer increases the stability index of the word and contributes to the stability index of the corresponding rule in the student model. When stability is reached the priority index for the word decreases, meaning that other (still unstable) words and rules are better candidates for the generation of forthcoming tasks.

Different support tools are offered to the student, e.g., perceptual support (e.g., auditory spelling, visual remarks), conceptual support (e.g., intuitive/mnemonic rules, visual associations), or syntactic information (e.g., grammatical rules). The content of the tools at any given time corresponds to the actual exercise (or target word), and can be activated by the student at her or his own pace (see Appendix A).

Appendix A : Example of screen layout for a spelling exercise

exercise (target word)

support tools (e.g., spelling, sound, image, rule)

 

A detailed report is generated for the teacher, who can review all information (e.g. learning process, performance) in varied configurations.


Subject

The spelling performance of a low vision child before, during and after working with the "Pupil" computer system was traced closely.

The subject, H., is an eighth grader. For reading and writing purposes she requires character enlargement up to 5 cm. from a working distance of 13 cm., by means of closed circuit TV in class and at home. Because she is not able to read from the blackboard her work in class is based on auditory information or the use of the enlargement TV system. She does word processing by blind typing.

Procedure

The study consisted of three stages: pre-assessment diagnostic session, treatment sessions and post-assessment diagnostic session.

 

Pre-assessment diagnostic session: At the beginning of the diagnostic session the subject received a short explanation about the system's features and operation and about its diagnostic tasks (e.g. dictation, sentences with blank fill-in spaces). The diagnostic session lasted about two hours and consisted of two sets of sentence-dictation tasks, the first including 98 words and the second 87 words, a total of 185 words.

Treatment sessions: Based on the diagnostic results a treatment plan was generated by the computer system focusing on the spelling rules on which the student showed poor performance.

At the beginning of the first session the subject was introduced to the features of the environment: types of tasks (see definition of task in the next section), available functions, available support tools (e.g., "read me again", visual hint, grammatical rule). The treatment consisted of 13 sessions, each lasting 20 minutes (a total of 4 hours and 20 minutes of training). During the treatment H. accomplished 31 tasks (total practice on 394 words). Each session included usually two tasks, and sometimes three (practice on 30 to 45 words).

Post-assessment diagnostic session: Similar in structure and number of tasks to the pre-assessment session, this session lasted about one hour and 15 minutes. At the end of this session an interview was conducted.

Data collection

Two data-collection instruments were used in this study. The first was a log mechanism built-in in the computer system which stored all subject/system interactions, together with relevant additional information (e.g., response-time, category of practiced rule and word). This material was used for updating the student model (e.g., known rules, stable/unstablewords, stable/unstable rules). The second data collection procedure was non-structured and non-interventional observation (Spindler, 1992) by which the subject's actions and comments (e.g., repeated reading of the target task shown on the screen, comments about system's features) were written down for further analysis.

In the pre- and post-assessment sessions data were collected for the following variables: (1) correctness of the answer; (2) response time, (3) reading out loud of the sentence by the subject before typing the target word; (4) request for repeated dictation of the exercise by the system. Data for variables 1,2, and 4 were automatically logged by the system, and for variable 4 were registered by the observer.

During the treatment stage data were collected regarding the following seven variables: (1) number of spelling mistakes; (2) kinds of spelling mistakes; (3) consistency of mistakes; (4) stabilization point of correct spelling; (5) response time in solving an exercise; (6) changes in writing tactics; (7) changes in use of the visual and auditory channels. Data for variables 1 to 5 were automatically logged by the system, and for variables 6 and 7 they were registered by the observer.

The results will be presented referring to the earlier mentioned research questions. The report of results focuses on the subset of rules (and corresponding words) for which data were obtained at the three stages of the study, namely pre-assessment, treatment, and post-assessment. The subset of rules (and words) identified in the pre-assessment as requiring remedial treatment, was used by the system for the design of the treatment plan, and was tested again at the end of the process in the post-assessment diagnostic session. In H's case the data relate to 39 words which are instances of the rules diagnosed in the pre-assessment as needing remedial treatment.

To answer this question we will refer first to the pre- and post-assessment data for the variables grade (correct / incorrect), response time (in seconds), reading out loud (percentage), and repeated dictation (percentage).

Table 1 shows the pre and post-assessment data for subject H.

Table 1: Pre/post diagnostic assessment

 

 

grade

(9)

response-

time

(mean in seconds)

 

reading

(9)

repeated

dictation

(9)

pre-test

54

32

31

33

post-test

72

19

97

7.7

 

**

*

**

*

*p<0.05 **p<0.01

The results in Table 1 show a significant increase in correct spelling performance (18%) and in number of times the subject read the sentence before answering (66%). A significant decrease was registered in mean response-time (23 seconds) and in number of repeated dictations of the exercise the subject requested (25%©).

Data collected during the training sessions indicate similar trends for the different variables. H's remedial plan included work on 39 words involving seven rules, for which the system generated 31 tasks (total 394 exercises). Table 2 shows data for the relevant variables by the first and the last task of each rule, e.g., 111/16 - 111/21 indicates that task number 16 was the first treatment assignment for rule 111 and task number 21 the last one for that rule. The next column in the Table presents the number of occurrences of words related to a given rule until the rule was considered stable by the system, e.g., 47 occurrences (namely exercises) for rule 111.

Table 2: Data collected during remedial treatment

 

Rule

 

grade

(9)

response-

time

(mean secs.)

 

reading

(9)

repeated

dictation

(9)

tool

use

(n)

occur-

rences

(n)

109/1

109/9

80

87

17

13

 

80

 

22

127

101/10

101/11

80

73

16

12

93

67

13

7

7

33

102/13

102/17

93

93

16

14

87

87

7

2

61

111/16

111/21

100

100

15

14

73

80

7

47

201Ø22

201/23

100

87

15

12

87

73

7

7

1

32

209/25

209/29

60

93

16

13

73

73

 

7

14

64

215/30

215/31

87

93

22

17

67

73

7

5

31

      

total = 394

The results show a significant increase in mean grade for three rules, and no change in the (already high mean) for two rules. On the other hand a decrease in performance was observed for two rules (101 and 201), but it should be noted that H. worked on these rules for only two tasks (about 30 exercises each rule) which makes for a very short treatment period. Response time decreased between the first and last task of each rule. The request for repeated dictation was low in general and even decreased in all cases but one (rule 209).

Two variables supply relevant information for this question: response time and correctness of the answer. Table 2 shows a cross tabulation between these variables for a total of 39 words.

  

response-time

 

 

 

decrease

increase

 

 

increase

6

3

 

grade

no difference

correct answer

15

4

 

 

no difference

mistake

7

2

 

 

decrease

1

1

 

The results show that for six words out of the nine for which H's spelling improved between the pre- and post-assessment, response time decreased. For a total of 21 words (out of 28 correct answers) correctly spelled by H. in the post-assessment response time decreased.

These data can be considered in line with the assumption that improvement in spelling performance together with decrease in response time implied an automation trend in H's spelling skills.

Regarding this research question we will refer to data collected for the variables grade¨response-time and tool-use. For practical and clarity purposes, the great amount of data collected will be summarized in the following analyses in either quantitative or qualitative form.

Figure 1 shows the overall pattern of the time-response curve for all training sessions. The curve is a succession of peaks and troughs which appear in two main blocks: the basic and most massive block at the bottom of the curve ranging from 5 to 15 seconds, and a series of peaks reaching response times of 25-60 seconds.

Figure 1: Overall pattern for the response-time during the remedial treatment

This result will be best understood by looking closely at a segment of the curve with focus on a given word, as in Table 3. The focus word is (scorn) with some of its derivatives . The word first appeared in session 14 exercise 3 (or 14/3), and was correctly spelled by H. in response-time (rt) of 15 seconds. The word appeared again three exercises later (according to the session generation mechanism) in 14/6, with rt=7". Word appeared in 14/8, rt=17". Word in 15/8, rt=68", misspelled, and again in 15/11 rt=22", 15/14 rt=9", 22/5 rt=20", all three times correctly spelled. We can learn from this segment that peaks in response-time occurred each time a new word appeared (as in 14/3, 14/8 or 15/8), or an already practiced word reappeared several exercises away from its last occurrence (as with which did not appear between tasks 15/14 and 22/5). Moreover, as a word becomes more stable (namely, correct spelling along successive exercises) rt decreases, until reaching steady value within the range of 5-15 seconds.

Table 4: Stabilization process of a word and its derivatives

exer-

cise

 

word

answer

correct/

incorrect

response-

time

 

reading

repeated

dictation

use of

tool

14/3

1

15

1

0

0

14/6

1

7

0

0

0

14/8

1

17

1

0

0

15/8

0

68

1

1

picture

15/11

1

22

1

0

0

15/14

1

9

0

0

0

22/5

1

20

1

0

0

22/14

1

6

1

0

0

23/10

1

10

1

0

0

23/15

1

12

1

0

0

Data in the table suggest a two-stage pattern. First there is a correspondence between misspelling and recurrent use of a support tool ("picture" or "read-me-again") as in task 15/8. Following the use of a tool after misspelling, the word is then correctly spelled and rt decreases.

Looking at the complete set of data of H's spelling performance on 394 word occurrences, we observed that the pattern takes two different forms. During the first part of the stabilization process the pattern can be depicted as in Figure 2a. Leaks in spelling performance (misspelling) correspond to peaks in rt and in tool use. During the treatment sessions H. asked for a support-tool 51 times, 84% of these after misspelling a word. Once words reach stable status the pattern can be depicted as in Figure 2b. Recourse to tools decreases, as well as rt, which remains in steady low value.

Figure 2: Patterns of correspondence between spelling, response time and use of support tools

2a

2b

 

Observations and interview data

Technical features. H. learned to work freely with the computer system within a short period of time. She defined the system settings according to her needs (letter size, foreground and background colors, sound volume) in the very first session and did not change these throughout the treatment. At about the third task H. showed confident and skillful manipulation of the system's tools and work modes, and even expressed these feelings to the observer.

Spelling strategies. Through the remedial process H. developed varied strategies for accomplishing the spelling tasks. In correspondence with her level of awareness to spelling mistakes, and mastery of appropriate (formal or intuitive) spelling rules, these strategies evolved and became more complex and accurate. Following are some meaningful stages in the development of H.'s strategies.

During the first stages H.'s response to spelling mistakes was to ask first for repeated dictation. Then she typed letter by letter hesitantly, while speaking each letter out loud. In these stages H. strongly relied on contextual information (namely the whole sentence into which the target practice word fits) for solving the spelling tasks.

By about the eighth task H's attitude towards contextual information started to change. When a target word first appeared she focused on its location within the sentence, carefully typing it while listening the auditory feedback. But in subsequent occurrences of the same word she typed fast typing, no longer needing the recurrent reading of the whole sentence.

During task 23 H. first turned to use a support tool before typing a word which she found difficult to spell. In general terms it seems that in the last portion of the remedial sessions H. adopted a more cautious approach in solving the tasks. When a target word which H. misspelled in the past appeared as part of aUnew exercise, she typed it carefully and revised its spelling before pressing the confirmation key.

Renewed awareness to visual information. Of particular interest was H's response to exposure to visual information and her discovery of its functional contribution to her work. The visual properties of the user interface were specifically designed for low-vision users (e.g., size and location of icons, scroll window for very large fonts), and individual users could adapt interface features (e.g., size of characters, foreground and background colors) to their specific needs. H. showed interest in these visual features and willingness to benefit from them from the very first stages of the remedial sessions; she also made this explicit to the observer.

The adaptation of display features to her needs allowed her to read the target words as many times as needed before typing. By the seventh session she had already developed her own navigation strategies through the icons and tools in the screen. When looking for a support tool, she managed to move efficiently among the icons (working at 8-10 cm distance from the screen) until reached the one she wanted. This visual mode allowed her to access and activate many different and powerful features effectively and inUa very short time. By the eighth session H. already followed visually and by sound both the computer dictation of the target word as well as her own typing of it.

Motivation. During all stages H. was completely concentrated on her work with the "Pupil" system. Whenever she had the option between starting a new task and quitting the system she chose to continue to work. She often asked for additional work; in her own words:..."the letters and the sounds helped me very much...I would like to continue myUwork with the computer". H. was a shy girl, and even if she did not often speak directly about the work process, her body language and gestures revealed her satisfaction.

When asked whether she felt that the remedial process had helped her in writing also in class and at home, she answered that she feels that ..."it helped, ...my spelling mistakes decreased...I enjoyed working with the Pupil system".

The lack of appropriate instruction and of quality support tools seriously affect the acquisition process of correct spelling by low-vision children. Given this population's high level of heterogeneity regarding vision capability and spelling knowledge and skills, it seems obvious that any solution to be devised should satisfy a comprehensive setUof requirements: individualized treatment according to vision and spelling levels; rich and varied alternative information channels to compensate for the impairment in the visual channel; adaptive generation of remedial plans to adjust to individual improvement paths; flexibility in time demand and composition of remedial tasks to accommodate to individual pace; continuous feedback by varied means to allow students to evaluate their own performance while they accomplish the tasks; high sensitivity toUnuances in students' learning and improvement process to ensure utmost possible adaptation to their needs.

The case study reported in this paper focuses on one student's work with a computer adaptive remedial tool developed along these lines and requirements. The first level of observation looked at the results of the system's comprehensive diagnostic test. The test offered a detailed picture of the child's spelling performance in a wide range of cases and spelling rules. In line with our previous pilotUdata we found that the most frequent category of misspelling in the diagnostic tasks was letter-shift, in particular among the Hebrew characters .

The next level of observation considered the effect of the work with the computer tool on the student's spelling performance. The pre- and post-test comparison showed significant improvement in spelling after completion of the remedial plan. A more detailed view however was obtained by collecting data on particular variables of the student's performance, e.g., response time, stabilization stages for words and rules, frequency and context of use of support tools. The more detailed picture thus obtained was equally encouraging. Let us consider several salient issues regarding changes in the student's performance.

Saimon and Saimon (1973, 1979) suggested three operational spelling modes, in correspondence with the amount and quality of knowledge a subject owns about a target word. These modes are (a) automatic retrieval, (b) spell-and-check cycle, and (c) phonetic writing (based on sound mimicry). The last mode has the highest potential for misspelling, particularly for letters with phonemical similarity. It is also the most frequently used by low-vision children, who do not rely on reference information from the visual channel but on incomplete (and often incorrect) word models stored in memory. The detailed data obtained in this case study show a clear evolution path in the student's performance. From phonetic writing in the first sessions theUstudent moved to process-writing, typing the word and checking it. Initially the student typed the word using her partial spelling knowledge, then she looked for help from the system's support tools, and finally evaluated the result according to correctness criteria stored in her memory. In the advanced stages of learning H. was able to automatically retrieve the spelling of the word. This process is reflected in the overall trend observed by correlating correctness (increasing) and time-response (decreasing) of the student's answers along the training process and between the pre- and post tests.

Another central issue concerns the role of the support tools in the system. The tool most frequently used by H. was "spelling" (literal auditory spelling of the target word), which she considered the fastest help. This tool seemed to have a twofold effect. The first and most obvious was that of supplying immediate help in solving the task. But in addition it served H. to develop models of the words by relating auditory information to visual information about particular letters and letter configurations. As H's work advanced she started to replicate the system's spelling by herself before and during the actual typing of the target word, in the process of the cognitive assimilation of the word as automatically-retrievable unit.

H's use of alternative information channels deserves special attention. As could be expected, support relying on the auditory channel was of great use. But as the work proceeded the use of the visual channel increased. The student spent increasing time reading the sentence before typing the target word (reinforcing the auditory information she got from the system). At the same time the demand for auditory support (e.g., repeated uttering of the target word by the system) decreased. This readiness for effective utilization of existent visual capability could be attributed both to features of the system enabling the adaptation of the working environment to individual needs and constraints (e.g., font size, font and background color), and to motivational aspects (e.g., sense of improvement, of effective interaction with the system, of varied support in solving the task).

A final remark should be made about our research agenda. This comprehensive case study led us to a preliminary set of conclusions which will serve for the formulation and planning of forthcoming systematic studies. We have already collected additional case-study information, as well as started a systematic study with a larger group of low-vision children. Examples of issues we are currently looking at are, among others: the differential contribution of the diverse types of tools (e.g. perceptual, conceptual, intuitive rules) on the students' spelling performance; a detailed tracing of the process by which automation in spelling and model word retrieval evolves; students' strategies of spelling acquisition and tool use; ability to generalize and apply spelling problem-solving, rules, and words models beyond the spellingUset targeted by the current version of the system.

As regard the role of advanced technology in supporting populations with special needs, we have no doubt that the "Pupil" system has the potential to contribute to low-vision children's' quality of learning and functioning in the literate environment.


 

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