Readiness of individuals with visual impairments for participation in distance education

Abstract

Individuals with visual impairments (VI) may face difficulties fitting in a distance education (DE) program mainly because of accessibility issues and a pervading lack of readiness. The main question was which difficulties and, specifically, which specific aspects of participants’ readiness could jeopardize their attendance in a DE program. The aims of this study were to examine the readiness of individuals with VI for participation in DE and the possible relationships between participants’ readiness for participation and their personal characteristics. A 42-item questionnaire has been developed to examine readiness for participation through five sub-scales referring to motivation, skills, self-management, interaction, and access to technological means (including means of assistive technology). The findings revealed a slightly positive degree of readiness as far as participants’ motivation, perception of their skills, and self-management are concerned and an approximate neutral degree of readiness regarding the aspects of interaction and access to technological means in the context of a DE program. The greater the educational level and the frequency of computer usage, the more positive the readiness for participation.

Keywords

Assistive technology distance education readiness visual impairments

Introduction

Unforeseen conditions, such as the coronavirus pandemic disease (COVID-19), may easily create the need for formal alternative educational modalities in every aspect and level of education—general and special, primary, secondary, and tertiary. Traditionally, distance education (DE) seems to be the appropriate choice of education model for those students who do not have the ability to attend a conventional classroom program (Liyanagunawardena & Hussain, 2017; Pant, 2005). Beyond the widely discussed advantages of DE (e.g., Alghazo, 2006; Bisciglia & Monk-Turner, 2002; Evans & Douglas, 2008; Graff, 2003; Madden et al., 2005; O’Malley & McCraw, 1999), special emphasis has been placed on the significance of various DE programs’ dimensions for individuals with disabilities. Specifically, e-learning methodology may prove to be an excellent opportunity for students with disabilities by offering flexibility and convenience, facilitation of communication, and greater adaptability to learners’ needs (Leporini & Buzzi, 2007), leading finally to access to education and social inclusion (Cinquin et al., 2019).

Furthermore, computer-mediated communication could contribute to social relationships and lead to an interaction unaffected by prejudices and preconceived notions emerging from physical attributes (Arrigo, 2005; Kirkwood & Price, 2005). As a result, students can participate being more unimpeded and focused than they are during face-to-face communication (Kirkwood & Price, 2005). Information and communications technologies (ICTs) may create opportunities for individuals with VI to interact with others and with learning materials through active communication, achieving both social and learning goals (Evans & Douglas, 2008).

Actually, new ICT mediates teaching and learning with a continually increasing rate (Kirkwood & Price, 2005) and is employed in DE for greater and more diverse population proportions to be included (Pant, 2005). These population proportions are differentiated from each other based on characteristics such as employment and the respective full-time/part-time course as well as geographic position (Kirkwood & Price, 2005). Similarly, Web-based education can accommodate students of various cognitive styles—field-dependent students, that is, students who prefer team working or are externally directed and accept ideas ab initio (Alomyan & Au, 2004). Thus, the use of new technology may be motivating for both the learners and the instructors, which in turn ensures a positive attitude toward DE from both (Pant, 2005).

However, when it comes to individuals with VI, the provision of ICT does not coincide with positive DE experiences mainly because of accessibility and compatibility issues (Mokiwa & Phasha, 2012). In a research examining the views of individuals with VI relative to the accessibility of online educational tools, 60% of the respondents declared that their experience using assistive technology to access these tools has ranged between “no successful use/access,” “unreliable/inconsistent,” and “doable with patience/effort” (American Foundation for the Blind [AFB], 2008). That is the reason why students with blindness may prefer tools that are accessible, such as teleconference, over more sophisticated tools that are incompatible with the assistive technology they normally use (Cain & Merrill, 2001).

Thus, it is necessary to incorporate individuals with impairments in the instructional design of computer-based educational programs (Sombrio et al., 2016) and to investigate how different learners’ needs are met and who are the students who best fit in technology-/Web-based educational environments (Erdogan et al., 2008). For instance, the familiarity with and the level of students’ access to ICTs evoke a challenge to the ability of an institution to avoid “digital division” (Kirkwood & Price, 2005). In the same way, students with greater confidence using online learning technology seem to attach less importance to the time they need to spend themselves and to challenges relative to social interaction, administrative, and instructor issues (Muilenburg & Berge, 2005). They may, also, feel more confident in dealing with unfamiliar problems (Lawless & Richardson, 2004) than those students who do not feel confident in managing online learning at all.

Knowing the students’ needs as well as the students’ strengths and weaknesses may result in better structuring the instructional materials and methodologies in a technology-based program, enabling students to feel included and instructors to be better prepared (Luque et al., 2018). Kinash et al. (2004) specifically mentioned in their review that combining theory and results of research for online learners with disabilities in practice will enhance the pedagogical aspects of online learning for all post-secondary learners. Furthermore, trying to become conscious of those students who are at risk of facing obstacles during the program, and becoming aware of the obstacles that could appear, should definitely result in preventing students’ dissatisfaction with or withdrawal from the program, or a disintegration of the program itself. In Richardson’s (2009) research, DE students with declared disabilities had lower prior qualifications and were more possible to obtain poorer degrees. In another research of Richardson (2015), students with VI in DE appeared to be less possible to complete a course and more possible to fail in the exams of a completed course than students without disabilities. Thus, researching the individual differences—with reference to various variables such as skills, perceived challenges, and motivation—among the future participants in a distance learning program can be the dawning of an attempt to better design the program.

In this way, Liao (2006) has suggested that in order to understand a learner’s flow toward distance learning, a number of other significant variables should be studied. Flow is defined as a psychological state in which people feel cognitively efficient, happy, and motivated (Csikszentmihalyi, 1990). The variables Liao (2006) studied are perceived skill, perceived challenge, perceived control, and perceived interactivity. Perceived skill refers to an individual’s capabilities of using computers (Liao, 2006) and managing systems and devices of distance learning, as these are assessed by the individuals themselves. In general, studying students’ attitudes toward computers could help someone see a student’s willingness or unwillingness to use computers as an instructional tool (Sanders & Morrison-Shetlar, 2001), while perceived skills of using computers were found to be related to the perception of the Internet’s usefulness in research and teaching (Liao, 2006). As the perceptions of skills move from beginner to expert, the Web is considered more useful for teaching and research (Applebee et al., 2000).

Perceived challenge refers to the level of challenge that an individual feels when he or she faces a distance learning system (Liao, 2006). Perceiving the hurdles that emerge during the use of a distance learning system as positive challenges is promising enough for someone not to abandon a distance learning program. Perceived control refers to the level of control that someone believes he or she has while dealing with a distance learning system (Liao, 2006). As with challenges, feeling that there is control over the quite complex systems of distance learning is essential to prevent withdrawal from the program. Perceived interactivity refers to human–computer interaction as judged by the learner (Liao, 2006). Human–computer interaction is defined as the design that should lead to the fit between the user, the computer, and the services, resulting in optimal performance (Te’eni, 2006, as cited in Karray et al., 2008). Human–computer interaction in the context of a DE program is of extreme importance, bearing in mind that the computer is the fundamental means of communication and contact between the involving individuals. However, human–computer interaction or learner–interface interaction, according to Hillman et al. (1994), is considered the fourth type of interaction, following the three types of interaction analyzed by Moore (1989) with reference to distance learning: learner–content interaction, learner–instructor interaction, and learner–learner interaction.

Especially, the learner–instructor interaction and the learner–learner interaction appear to be very important for participants’ attitudes toward DE. The widely discussed issue of isolation when it comes to distance learning, in comparison with traditional learning (Moore & Thompson, 1990), attaches particular importance to the subject of interaction in a distance learning program. Tekinarslan (2008), who studied the attitudes of Turkish distance learners toward Internet-based learning, found that while learners have, in general, positive attitudes toward and enjoy this mode of studying, there may be a preference for face-to-face communication. Similarly, Kim et al. (2012) revealed that although students’ satisfaction cannot be predicted by the program modality (DE or on-campus), learner–learner’s and faculty–learner’s interaction can. In addition, according to Joosten and Cusatis (2019), underrepresented students (including students with disabilities) seem to ascribe considerable importance to learner–instructor interaction, which can actually predict students’ perception of learning.

Eventually, it becomes obvious that the importance of interaction, perception of personal skills, experience with computers, frequency of the Internet and ICT use, and motivation with reference to distance learning is undeniable when DE’s success is under the microscope. Taking this into consideration, it appears to be necessary to develop a tool that could address and evaluate all these factors that influence both the readiness of a student (with and without impairments) and, by extension, the evolution of a distance learning program.

McVay (2001) developed a 13-item questionnaire to assess readiness for online learning. The questionnaire basically assesses students’ learning independence as well as students’ comfort with some features of online learning. Bernard and his colleagues (2004) extended McVay’s questionnaire with the idea that it was necessary to assess a few more features of online learning such as the nature of, effectiveness of, and desire for interactivity. They finally developed a 38-item questionnaire including the 13 items of McVay’s questionnaire. The 38 items fall into four categories: (a) readiness for online skills, (b) readiness for self-management, (c) readiness for beliefs about DE/online learning, and (d) desire for interaction with an instructor and/or other students (Bernard et al., 2004).

Similarly, in this study, readiness is the main axis. Motivation, personal skills, self-management, interaction, and access to technological means that might influence someone’s readiness for participation in a DE program are the factors under investigation. This study constitutes the first trial to create a tool appropriate for the examination of readiness in individuals with VI. Previously, the attitudes of individuals with VI toward DE have been studied (Koustriava & Papadopoulos, 2014) to cover the affective and cognitive characteristics (i.e., components of attitude) of learning. The tool detecting readiness of individuals with VI to participate in a DE education completes the study of attitudes, and both tools could work in conjunction to secure that a DE program will not fail because of students’ lack of internal preparation.

The study

The main question that arose was which specific aspects of participants’ readiness—motivation, personal skills, self-management, interaction, and access to technological means—could jeopardize their attendance in a DE program and in what degree. Hence, the aims of this study were to examine (a) the readiness of individuals with VI for participation in a DE program with reference to the above-mentioned aspects, and (b) the possible relationships between participants’ readiness for participation and their personal characteristics (age, age at onset, gender, vision status, frequency of computer usage, and employment).

It is worth noting that in this study the terms “distance education,” “e-learning/online learning” and “Web-based education” are considered synonymous (although they are not) and are used interchangeably. It is the author’s general position that the DE of the 21st century—especially when it is studied through the prism of special education—is structured upon the Web and the Internet, contemporary technological means, and ICTs. The reasons for this are that the Internet has the potential to offer an environment of justice where people of different cultures, languages, religions, gender, and abilities can participate on equal footing. It is also suggested to have a considerable effect on learning attitude since it can reinforce motivation and interest in learning (Erdogan et al., 2008).

Method

Participants

The sample consisted of 41 adults with VI (27 males and 14 females), who ranged in age from 20 years and 8 months to 39 years and 11 months (M = 28.19, SD = 5.14) and who were residents of the two most crowded cities of Greece, Athens and Thessaloniki. The participants were recruited from the members of the Panhellenic Association of the Blind. Initially, we contacted by phone a random selection of 60 individuals with VI to invite them to participate in the study. From this group, 41 individuals agreed to participate and gave consent to proceed. None of the participants had any previous experience of DE.

Twenty-one participants (51.2%) were individuals with blindness or individuals with severe VI (visual acuity <20/400), and 20 participants (48.8%) were individuals with low vision (20/200 > visual acuity > 20/400). Thirteen of the 41 participants (31.7%) had congenital VI, while in the remaining 28 participants (68.3%) the impairment appeared after the age of 6 years.

With reference to their level of education, 26 participants (63.4%) had a high school diploma, 14 participants (31.4%) had a university degree, and 1 participant had finished middle school. Twenty-two (53.7%) subjects stated that they had a job, 18 (43.9%) participants stated that they were unemployed, and 1 was retired.

The participants were also asked about the use of computers. All of them owned a computer, while approximately half of them (51.2%) used it for more than 2 hr a day, 14 participants (34.1%) used it for 1–2 hr a day, 5 participants (12.2%) used it for 1–2 hr per week, and 1 participant did not use it at all.

Instruments—procedure

A self-constructed questionnaire measuring the readiness for participation in a DE program of individuals with VI was employed in the research. The procedure was structured upon the following stages: (a) a thorough study of the relative bibliography was conducted, leading to a pool of items; (b) an initial questionnaire was developed, including 30 closed-ended questions concerning the readiness for participation in a DE program; (c) this initial questionnaire was administered to 300 sighted adults; (d) exploratory factor analysis was applied, leading to a short questionnaire of 25 closed-ended questions; (e) in this short questionnaire, 17 questions addressing the educational needs of individuals with VI and the conditions under which individuals with VI are educated were added and a final questionnaire of 42 closed-ended questions arose; and (f) this final questionnaire was administered to the participants of this study. It should be clarified that the 17 items concerning the needs of individuals with VI had not been included to the initial questionnaire of 30 items because the latter would be administered to sighted adults who should not answer these 17 questions.

The initial thorough literature review resulted in a pool of items surfaced from questionnaires and papers (e.g., Atan et al., 2004; Bernard et al., 2004; Liao, 2006; McVay, 2001; Mishra & Panda, 2007; O’Malley & McCraw, 1999; Tekinarslan, 2008) contiguous with readiness for participation in a DE, an online learning, or a Web-based training program.

The participants answered the questionnaire based on a 5-point Likert-type scale: totally disagree, disagree, don’t know (noting that this answer could be chosen in both cases of “neither disagree nor agree” and “don’t know”), agree, and totally agree. There were positively formulated (see, for example, Q5, Q8) and negatively formulated (see, for example, Q7, Q9) items. To calculate the total score for each participant and for all the participants in aggregation, the positively formulated items were scored as follows: totally disagree = −2, disagree = −1, don’t know = 0, agree = 1, and totally agree = 2. The negatively formulated items were scored reversely (e.g., totally disagree = 2).

Since it was very difficult to recruit the required number of participants with VI in Greece to perform a factor analysis, it was considered appropriate to alternatively address the questionnaire to sighted individuals implementing convenience sampling. The questionnaire was sent via e-mail to 500 sighted individuals, and 300 of them answered it. The majority of them were university students or graduates aged from 19 to 45 (M = 24.90, SD = 6.37) years. The process of answering the questionnaire was completed from a distance. For the development of the questionnaire into a digital form, Adobe LiveCycle Designer software was used.

A factor analysis was conducted using a principal component factor analysis, followed by a varimax rotation. Implementation of the varimax rotation with 25 items having a factor loading greater than .32 (Tabachnick & Fidell, 2001) revealed five factors that accounted for 57.69% of the variance. The resulting factor loadings for each of these 25 items and the resulting percentages of variance for the five factors emerged can be seen in Appendix 1.

The questionnaire formed after having completed the factor analysis included 25 items: 5 items concerning participants’ motivation to participate in a DE program, 8 items concerning participants’ perception regarding their skills to manage technological means of DE, 3 items concerning participants’ perception regarding their ability to manage themselves in the context of a DE program, 5 items concerning participants’ technology-based communication skills within a DE program, and 4 items referring to participants’ access to basic technological means necessary for a computer-/Internet-based DE program. This 25-item questionnaire was enriched by 17 more questions (Qi, Qii, etc.; see Appendix 1) pertaining to the means of assistive technology that individuals with VI use to satisfy their educational and everyday life needs. From these 17 specialized questions, 1 was added to the first sub-scale (Readiness: Motivation) and the remaining 16 questions were added to the fifth sub-scale (Readiness: Access to technological means). It should be noted that in case a question referred to assistive technology that the subject could not use anyway (for instance, a question referring to a closed-circuit television [CCTV] when an individual with blindness was answering the question), that same question was omitted and was not calculated in the participant’s score.

Completing the above-described process, the final questionnaire consisted of 42 items divided into five sub-scales in respect of the five factors that emerged from the factor analysis: (a) Readiness: Motivation (sub-scale 1), (b) Readiness: Skills (sub-scale 2), (c) Readiness: Self-management (sub-scale 3), (d) Readiness: Interaction (sub-scale 4), and (e) Readiness: Access to technological means (including means of assistive technology) (sub-scale 5). The title of each sub-scale ensued after taking into consideration both the items with the highest loading and the theoretical components of readiness.

Considering the scoring of participants’ answers (as described above), the higher the total score, the greater the degree of participants’ readiness appeared, whereas the closer to 2 the mean score per item, the readier (positive) the participants seemed to be. Conversely, the lower the total score, the lower the degree of participants’ readiness appeared, whereas the closer to −2 the mean score per item, the less ready (negative) participants seemed to be. The score for each sub-scale was interpreted in the same way.

Demographic data for the participants (gender, age, age at loss of sight, vision status, educational level, employment status, frequency of computer usage) were also collected.

Prior to starting to complete the questionnaire, participants were informed of the DE delivery mode through the reading of text. This text consisted of 210 words and included the following: (a) a complete definition of DE, (b) a description of synchronous (live and online) and asynchronous (offline) ways of teaching, (c) a reference to the possible ways of the realization of DE (fully by distance or in combination with on-campus meetings), and (d) references to the main advantages and disadvantages of DE, based on sources (published papers and books) of the relative scientific field.

The researcher read out the questions to the participant and then wrote down the answers given by him or her. Each participant took part in the process separately in a quiet room, and no other person except the participant and the researcher was in the room each time.

Reliability

Τhe reliability information of the questionnaire used in this study was as follows:

The test–retest reliability coefficients regarding the total questionnaire and the first, second, third, fourth, and fifth sub-scale were as follows: r = .90, r = .74, r = .96, r = .91, r = .71, and r = .98, respectively. The retest process took place 2 weeks after the completion of the test process. Twenty-four participants took part in the retest process.

Cronbach’s alpha coefficients regarding the total questionnaire and the first, second, third, fourth, and fifth sub-scale were as follows: α = .86, α = .72, α = .80, α = .72, α = .69, and α = .79, respectively.

Statistical analyses

To determine the predictors of readiness (as a whole) for participation in a DE program as well as the predictors of each sub-scale individually, two analyses were performed. First, the Pearson’s (r) correlation coefficients were calculated between the total score and the score on each sub-scale separately, and age, age at onset, gender, vision status (blindness vs low vision), frequency of computer usage, and employment (employed vs not employed). We did not use the term “unemployed” because we wanted to include all persons who were not in employment status, such as school or university students.

Then, the variables that showed statistically significant or close to significant correlations with the total score or the score on each sub-scale were used as possible predictors in six linear multiple regression analyses to define the predictors of readiness for participation in a DE program and the predictors of each sub-scale too.

Results

Initially, the means and standard deviations (SDs) of the total score and the score on each sub-scale separately were calculated. The results are presented in Table 1. Moreover, dividing the total score and the score on each sub-scale by the number of questions, the mean scores of the participants per item for the total of the questionnaire (see the right column of Table 1) as well as for each sub-scale resulted. Considering these mean scores and the Likert-type scale on which the answers were based, the participants’ answers revealed a slightly positive degree of readiness for participation in a DE program (M = 0.67) and a positive degree of readiness based on participants’ motivation (M = 0.98), their possession of required skills (M = 0.99), and their self-management (M = 0.98)—the first, second, and third sub-scale, respectively. Furthermore, the participants’ answers revealed a neutral degree of readiness as far as the interaction conditions of a DE program (M = 0.19) and the participants’ access to technological means (M = 0.24) are concerned—the fourth and fifth sub-scale, respectively.

Table 1.

Participants’ mean score per sub-scale and for the questionnaire in total.

	Mean	SD	Number of questions	Mean per item
Sub-scale 1	5.85	3.28	6	0.98
Sub-scale 2	7.88	4.26	8	0.99
Sub-scale 3	2.93	2.10	3	0.98
Sub-scale 4	.93	2.45	5	0.19
Sub-scale 5	2.51	8.47	20	0.24
Total	20.10	14.31	42	0.67

SD: standard deviation.

Table 2 presents the Pearson’s (r) correlation coefficients between the total score on the questionnaire and the score on each sub-scale, and age, age at loss of sight, gender, vision status (blindness vs low vision), educational level, frequency of computer usage, and employment (employed vs not employed).

Table 2.

Correlations between total score, score on each of the five SS, and participants’ personal characteristics.

	SS 1	SS 2	SS 3	SS 4	SS 5	Total
Gender	.191	−.224	.248	.001	−.093	−.041
Age	.358*	−.031	.074	.187	−.166	.017
Age at loss of sight	.108	−.041	−.102	.009	−.344*	−.205
Vision status	.081	−.037	−.031	−.108	−.040	−.039
Educational level	.334*	.445**	.501**	.429**	.183	.465**
Employment status	.027	−.008	.033	.391*	−.121	.004
Frequency of computer usage	.172	.558**	.379*	−.047	.246	.399**

SS: sub-scale.

p < .05; **p < .01.

Moreover, the linear multiple regression analysis method was implemented to predict the “Readiness for participation in a DE program” in total as well as the components of readiness that each sub-scale examines (see Tables 3 to 8).

Table 3.

Multiple regression for variables as predictors of “Readiness for participation in a DE program.”

Variable	B	Standard error	β	t	p
Educational level	11.754	3.633	0.428	3.235	.003
Frequency of computer usage	5.786	2.157	0.355	2.683	.011

DE: distance education.

Adjusted R² = .306.

p < .01.

Table 4.

Multiple regression for variables as predictors of “Readiness: Motivation.”

Variable	B	Standard error	β	t	p
Age	0.181	0.100	0.280	1.800	.080
Educational level	1.622	0.982	0.257	1.652	.107

Adjusted R² = .144.

p < .05.

Table 5.

Multiple regression for variables as predictors of “Readiness: Skills.”

Variable	B	Standard error	β	t	p
Educational level	3.205	0.976	0.392	3.285	.002
Frequency of computer usage	2.512	0.579	0.518	4.337	.000

Adjusted R² = .436.

p < .01.

Table 6.

Multiple regression for variables as predictors of “Readiness: Self-management.”

Variable	B	Standard error	β	t	p
Educational level	1.881	0.526	0.467	3.574	.001
Frequency of computer usage	0.791	0.312	0.330	2.531	.016

Adjusted R² = .325.

p < .01.

Table 7.

Multiple regression for variables as predictors of “Readiness: Interaction.”

Variable	B	Standard error	β	t	p
Educational level	2.022	0.621	0.430	3.256	.002
Employment status	1.907	0.641	0.392	2.972	.005

Adjusted R² = .303.

p < .01.

Table 8.

Multiple regression for variables as predictors of “Readiness: Access to technological means.”

Variable	B	Standard error	β	t	p
Age at loss of sight	−0.294	.120	−0.358	−2.448	.019
Frequency of computer usage	2.556	1.410	0.265	1.813	.078

Adjusted R² = .146.

p < .05.

Concerning the “Readiness for participation in a DE program,” in total the analysis yielded an adjusted R² of .306 (F = 9.8325, p < .01). Significant individual predictors of “Readiness for participation in a DE program” were educational level (β = 0.428, p < .01) and frequency of computer usage (β = 0.355, p < .05) (see Table 3). Age and educational level were included in a linear multiple regression analysis to predict the score on the first sub-scale. Regarding the score on the first sub-scale, the analysis yielded an adjusted R² of .144 (F = 4.285, p < .05), while no significant individual predictors of the score on the first sub-scale arose (see Table 4). Concerning the score on the second sub-scale, the analysis yielded an adjusted R² of .436 (F = 16.430, p < .01). Significant individual predictors of score on the second sub-scale were educational level (β = 0.392, p < .01) and frequency of computer usage (β = 0.518, p < .01) (see Table 5).

Concerning the score on the third sub-scale, the analysis yielded an adjusted R² of .325 (F = 10.625, p < .01). Significant individual predictors of score on the third sub-scale were educational level (β = 0.467, p < .01) and frequency of computer usage (β = 0.330, p < .05) (see Table 6). Regarding the score on the fourth sub-scale, the analysis yielded an adjusted R² of .303 (F = 9.697, p < .01). Significant individual predictors of score on the fourth sub-scale were educational level (β = 0.430, p < .01) and employment status (β = 0.392, p < .01) (see Table 7). Age at loss of sight and frequency of computer usage were used as possible predictors of the score on the fifth sub-scale. The analysis yielded an adjusted R² of .146 (F = 4.419, p < .05). The significant individual predictor of the score on the fifth sub-scale was age at loss of sight (β = −0.358, p < .05) (see Table 8).

The greater the educational level, the higher the degree of “Readiness for participation in a DE program,” and the greater the frequency of computer usage, the higher the degree of “Readiness for participation in a DE program” is as well.

Discussion

This study substantiated an attempt to elicit the degree of readiness for participation in a DE program from individuals with VI. At the beginning of this course, it had been considered necessary to develop a questionnaire that would be suitable for administering to individuals with VI. An innovative element of this study is the construction of an instrument that is adapted to the educational needs and educational conditions that visual impairment entails.

The analysis of the data collected revealed a slightly positive degree of readiness for participation in a DE program. The degree of readiness for participation is positive when readiness is examined on the basis of motivation, possession of required skills, and self-management. Bernard and his colleagues (2004)found that, among others, self-management is a positive predictor of achievement in a DE program. Similarly, Muilenburg and Berge (2005) studied the literature and realized that perceptions of self-efficacy in a distance learning program constitute one of the factors that could affect the learning outcomes. In this study, the positive degree was an expected result, although someone could anticipate a higher degree of readiness regarding the above aspects of readiness as well, since none of the participants had had a previous negative experience of DE. However, it is worth noting that the participants had had no previous experience of DE whatsoever. Thus, emotions of uncertainty and discomfort that usually appear with something unknown should account for the moderated degree of readiness to participate in a future DE program. Besides, Muilenburg and Berge (2005) highlighted the fact that in their research, students who had never participated in an online learning program appeared to foresee more problems than those who had had such an experience, while Lawless and Richardson (2004) pointed out that those students who have had previous experience of online learning appeared to be more confident.

Furthermore, the analysis revealed a neutral degree of readiness when the interaction conditions of a DE program and the participants’ access to technological means were examined. Concerning the interaction, the possibility of not getting a very positive degree of readiness for participation could not be an unforeseen result since a well-established general perception is that DE, Internet-based education, or computer-mediated education usually entails alienation risks, lack of personal contact (Bernard et al., 2004; Moore & Thompson, 1990), and accessibility issues (AFB, 2008). Since this is a widely held notion, it was expected that it might pervade the views of some participants, especially in the case of this study where participants had no previous experience of DE. In the study by Bernard and his colleagues (2004), participants appeared more negative about their desire for interaction after having an experience of participation in a Web-based course, which according to the researchers accounted for an overestimation of the importance of interaction in online learning. Similarly, Muilenburg and Berge (2005) reported that individuals with greater confidence in managing online learning tools seem to attach less importance to alienation challenges. However, interaction in the context of DE is a broadly discussed issue in the literature, with the majority of studies concluding that interaction is indeed an important aspect of DE. Liao (2006) concluded that interactivity is a more important variable of DE’s effectiveness than participants’ skills, participants’ perception of challenge, and their sense of control. The need for interaction opportunities and face-to-face communication may exist even if there is a general satisfaction with the other aspects of a DE program (Tekinarslan, 2008). This need may have a particular impact on cases where among students of a DE program there are individuals who are differentiated from others either because of their cultural characteristics (Thompson & Ku, 2005) or because of their impairment, but this is something that needs further investigation.

Moreover, the neutral degree of readiness because of participants’ access to technological means is quite a reasonable finding. In this sub-scale, there were positive answers to the questions regarding the ability to use and the access to technological means (such as the Internet and scanner devices) and specialized software that individuals with VI use to access the computer (such as a screen reader and screen magnifier). There were, however, negative answers to the questions regarding the ability to use, and the access to, specialized devices such as Braille display, CCTV, notetaker (e.g., Braille ‘n Speak), touchpad (e.g., IVEO), thermoform, and tactile image enhancer (e.g., PIAF). The cost to purchase many of these devices is too high to be covered individually, which restricts access only to specific settings (e.g., centers for individuals with VI, institutions, and libraries). In addition, many of these tools pose compatibility issues that could reduce users’ interest for engagement in DE (Mokiwa & Phasha, 2012).

In addition, searching for the possible relations between personal characteristics (gender, age, age at loss of sight, vision status, educational level, employment status, and frequency of computer usage) and readiness—in total or as far as specific components (motivation, skills, self-management, interaction, access to technological means) are concerned—three factors appeared to be significant: the educational level and the frequency of computer usage for readiness in total and the majority of its components, and age at loss of sight for access to technological means. With reference to educational level, a basic assumption is that as individuals complete the basic studies such as higher education or tertiary education, they probably feel more motivated and self-confident, and more willing to engage in flexible education delivery modes. In addition, the higher someone’s educational level is, the greater his or her self-management ability should be, and the more familiar with facing challenges in educational contexts he or she should feel. O’Malley and McCraw (1999) suggested that one of the three axes which influence the perceived effectiveness of distance learning is prior educational conditions.

Particularly anticipated was the finding of frequency of computer usage being a predictor of readiness for participation in a DE program. It is understandable that the more experienced someone is in the use of computers, the Internet, and advanced technological means such as ICTs, the more comfortable and readier he or she would feel to participate in a technology-based distance learning program. Prior computer experience seems to influence students’ attitudes toward computers (see Alghazo, 2006, for a review), while students’ attitudes toward computers could signify their willingness or unwillingness to use computers as an instructional tool (Sanders, Morrison-Shetlar, 2001). Moreover, it seems that the more someone uses the Internet, the more positive his or her attitude toward Web-based education can be (Erdogan et al., 2008). Regarding the age at vision loss as predictor of access to technological means, someone could speculate that the later the vision ability is getting lost, the more the introduction to assistive technology is delayed. As a result, the greater the delay, the lower the knowledge and the familiarization with assistive technology. In this case, an individual should appear less eager to participate in a program that is regulated by assistive technology.

To conclude, the above results concerning motivation, personal skills, self-management, interaction, and access to technological means form a total understanding of participants’ readiness for a DE program. It is extremely important for a program’s success to examine these dimensions of readiness prior to the beginning so as to prevent participants from quitting the program. Besides, aligning with the accessibility and inclusion suggestions of individuals with VI themselves could lead to the better preparation of both the DE program and the instructors (AFB, 2008; Luque et al., 2018).

The tool presented in this study derived from the first trial to examine readiness for DE in individuals with VI. In conjunction with the tool that investigates the attitudes of individuals with VI toward DE (Koustriava & Papadopoulos, 2014), a complete examination tool has been developed to discern the internal preparation of individuals with VI before they are engaged in a DE program. DE delivery entities should examine their future participants’ attitudes and readiness for participation, and inform and prepare them relatively. In this way, programs aiming at fully including individuals with VI into their curriculum could be designed in a student-centered perspective upgrading significantly the quality of their services and adding merit to their outputs. Moreover, Higher Education Institutions (HEIs) around the world have changed their educational practices because of COVID restrictions on face-to-face education. DE programs have been spread, and the need to include individuals with impairments is mandatory. Accessibility units or relative HEIs should be able to prepare the teaching staff and the students with VI according to their specific needs and possibilities. The tool presented here could definitely support this effort.

Moreover, future research should try to explore other dimensions of internal preparation for individuals with VI and enrich these tools, or even develop similar tools to address the needs of individuals with different educational needs or impairments. Finally, it would be useful to compare the results in these inquiring tools—addressed to individuals prior to their participation in a DE program—with the results of the program with reference to participants’ performance, satisfaction, and educational convenience.

Limitations

A limitation of this study is the lack of factor analysis implementation of the initial questionnaire on a sample of individuals with VI. Future research should attempt to apply a confirmatory factor analysis of the final questionnaire on participants with VI.

Footnotes

Appendix

Appendix 1

Item wording	Factor loadings	Percentage of variance
Factor 1: Readiness: Motivation		25.48
Q1. The thought of participating in a DE program challenges me.	.694
Q2. Written communication would support the improvement of skills such as typing speed, dictation, and syntax	.515
Q3. I believe that Internet-based education would be a motivation for learning.	.770
Q4. The ability to participate in DE program constitutes motivation for further training and specialization.	.732
Q5. I believe that participating in a DE program would support my professional career and development.	.757
Qi. The systematic use of special software (screen reader, screen magnifier, etc.) during the DE program will develop my knowledge of computers and will help me use the computer further in my everyday life.
Factor 2: Readiness: Skills		13.99
Q6. I communicate through computers (electronically) without any difficulty.	.712
Q7. I believe it would be difficult to handle a DE system and devices and that would frustrate me.	.797
Q8. I believe I could learn easily how to use a DE system and devices.	.800
Q9. I know less about using the Web than an average user.	.753
Q10. I am familiar with basic computer use and I know how to save a file with a different name, a different extension, and in a different place and to solve problems (e.g., “freezing”).	.699
Q11. I am familiar with basic Internet and Web activities (e.g., search with the use of a Web browser, send and receive e-mails with or without attached files, download and install software).	.746
Q12. I believe I can control the DE system and I feel calm about the thought of participating in a DE program.	.705
Q13. I can be a member of a team, and cooperate and discuss with the other team members during online activities.	.511
Factor 3: Readiness: Self-management		8.01
Q14. I am capable of managing my time effectively for study and delivering my papers on time.	.648
Q15. If something is very difficult for me or I cannot understand it, I usually prefer to stop trying.	.821
Q16. Whenever I start something, I always complete it.	.776
Factor 4: Readiness: Interaction		5.33
Q17. DE does not enable my participation in discussions with the members of the class because I find it difficult to communicate in the absence of face-to-face contact.	.665
Q18. The absence of participants’ physical presence during the teaching process would help me in posing and answering questions, making comments, and expressing my opinion in general.	.672
Q19. DE improves the communication between the members of a class because of the ability to communicate synchronously and asynchronously.	.526
Q20. The communication channels (e.g., e-mails, discussion groups, chat rooms, fora) that might be included in a DE program could satisfy my communication needs.	.687
Q21. I believe that the lack of physical contact in the context of a DE program would only lead to temporal social relationships.	.481
Factor 5: Readiness: Access to technological means		4.89
Q22. I have access to the Internet on a regular basis.	.793
Q23. I access the Internet easily when this is necessary for my studies.	.627
Q24. I have a high-speed Internet connection that supports quick file transference and reception.	.815
Q25. I have access to a scanner always or whenever this is necessary for my studies.	.468
Qii. I know how to translate a digital text in Braille and to emboss it
Qiii. I have access to a Braille embosser or I could find if it was necessary for my studies.
Qiv. I know how to use a screen reading software
Qv. I have access to a screen reading software or I could find if it was necessary for my studies.
Qvi. I know how to use a screen magnifying software.
Qvii. I have access to a screen magnifying software or I could find if it was necessary for my studies.
Qviii. I know how to use a Braille display.
Qix. I have access to a Braille display or I could find if it was necessary for my studies.
Qx. I know how to use a CCTV.
Qxi. I have access to a CCTV or I could find if it was necessary for my studies.
Qxii. I know how to use notetakers.
Qxiii. I have access to a notetaker or I could find if it was necessary for my studies.
Qxiv. I know how to use a touch pad (or a device for audio-tactile images).
Qxv. I have access to a touch pad or I could find if it was necessary for my studies.
Qxvi. I know a method to emboss/create tactile images.
Qxvii. I have access to tactile-images developing devices or I could find if it was necessary for my studies.

DE: distance education; CCTV: closed-circuit television.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Eleni Koustriava

References

American Foundation for the Blind. (2008). Distance learning: How accessible are online educational tools. http://www.afb.org/info/programs-and-services/public-policy-center/education-policy/distance-learning-how-accessible-are-online-educational-tools-4492/1235

Alghazo

I. M.

(2006). Student attitudes toward web-enhanced instruction in an educational technology course. College Student Journal, 40, 620–630.

Alomyan

(2004). Exploration of instructional strategies and individual difference within the context of web-based learning. International Education Journal, 4(4), 86–91.

Applebee

Clayton

Pascoe

Bruce

(2000). Australian academic use of the Internet: Implications for university administrators. Internet Research: Electronic Networking Applications and Policy, 10(2), 141–149.

Arrigo

(2005). E-learning accessibility for blind students. In Mendez-Vilas

Gonzalez-Pereira

Mesa Gonzalez

J. A.

(Eds.), Third international conference on multimedia and information & communications technologies in education: “Recent research developments in learning technologies” (pp. 422–426). Formatex.

Atan

Rahman

Z. A.

Idrus

R. M.

(2004). Characteristics of the web-based learning environment in distance education: Students’ perceptions of their learning needs. Educational Media International, 41(2), 103–110.

Bernard

R. M.

Brauer

Abrami

P. C.

Surkes

(2004). The development of a questionnaire for predicting online learning achievement. Distance Education, 25(1), 31–47.

Bisciglia

M. G.

Monk-Turner

(2002). Differences in attitudes between on-site and distance-site students in group teleconference courses. American Journal of Distance Education, 16(1), 37–52.

Cain

H. M.

Merrill

(2001). Distance education for master’s students with visual impairments: Technology and support. Journal of Visual Impairment & Blindness, 95(9), 572–575.

10.

Cinquin

P. A.

Guitton

Sauzéon

(2019). Online e-learning and cognitive disabilities: A systematic review. Computers & Education, 130, 152–167.

11.

Csikszentmihalyi

(1990). Flow: The psychology of optimal experience. Harper & Row.

12.

Erdogan

Bayram

Deniz

(2008). Factors that influence academic achievement and attitudes in web based education. International Journal of Instruction, 1(1), 31–48.

13.

Evans

Douglas

(2008). E-learning and blindness: A comparative study of the quality of an e-learning experience. Journal of Visual Impairment & Blindness, 102(2), 77–88.

14.

Graff

(2003). Cognitive style and attitudes towards using online learning and assessment methods. Electronic Journal of e-Learning, 1(1), 21–28.

15.

Hillman

C. A.

Willis

D. J.

Gunawardena

C. N.

(1994). Learner-interface interaction in distance education: An extension of contemporary models and strategies for practitioners. The American Journal of Distance Education, 8(2), 30–43.

16.

Joosten

Cusatis

(2019). A cross-institutional study of instructional characteristics and student outcomes: Are quality indicators of online courses able to predict student success? Online Learning, 23(4), 354–378.

17.

Karray

Alemzadeh

Abou Saleh

Arab

M. N.

(2008). Human-computer interaction: Overview on state of the art. International Journal on Smart Sensing and Intelligent Systems, 1(1), 137–159.

18.

Kim

D. S.

Lee

Skellenger

(2012). Comparison of levels of satisfaction with distance education and on-campus programs. Journal of Visual Impairment & Blindness, 106(5), 275–286.

19.

Kinash

Crichton

Kim-Rupnow

W. S.

(2004). A review of 2000-2003 literature at the intersection of online learning and disability. American Journal of Distance Education, 18(1), 5–19.

20.

Kirkwood

Price

(2005). Learners and learning in the twenty-first century: What do we know about student’s attitudes towards and experiences of information and communication technologies that will help us design courses? Studies in Higher Education, 30(3), 257–274.

21.

Koustriava

Papadopoulos

(2014). Attitudes of individuals with visual impairments towards distance education. Universal Access in the Information Society, 13, 439–447.

22.

Lawless

Richardson

J. T. E.

(2004). Monitoring the experiences of graduates in distance education. Studies in Higher Education, 29(3), 353–374.

23.

Leporini

Buzzi

(2007). Learning by e-learning: Breaking down barriers and creating opportunities for the visually-impaired. In Stephanidis

(Ed.), Universalaccess in human-computer interaction: Application and services (pp. 687–696). Springer.

24.

Liao

L.-F.

(2006). A flow theory perspective on learner motivation and behavior in distance education. Distance Education, 27(1), 45–62.

25.

Liyanagunawardena

T. R.

Hussain

(2017). Online distance education materials and accessibility: Case study of University College of Estate Management. In Vincenti

Bucciero

Helfert

Glowatz

(Eds.), e-Learning, e-Education, and online training: Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (pp. 79–86). Springer.

26.

Luque

Brandão

L. O.

Kira

Brandão

A. A.

(2018). On the inclusion of learners with visual impairment in computing education programs in Brazil: Practices of educators and perceptions of visually impaired learners. Journal of the Brazilian Computer Society, 24(4), 1–12.

27.

Madden

Ford

Miller

Levy

(2005). Using the internet in teaching: The views of practitioners (A survey of the views of secondary school teachers in Sheffield, UK). British Journal of Educational Technology, 36(2), 255–280.

28.

McVay

(2001). How to be a successful distance education student: Learning on the Internet. Prentice Hall.

29.

Mishra

Panda

(2007). Development and factor analysis of an instrument to measure faculty attitude towards e-learning. Asian Journal of Distance Education, 5(1), 27–33.

30.

Mokiwa

S. A.

Phasha

T. N.

(2012). Using ICT at an open distance learning (ODL) institution in South Africa: The learning experiences of students with visual impairments. Africa Education Review, 9(1), 136–151.

31.

Moore

M. G.

(1989). Three types of interaction. The American Journal of Distance Education, 3(2), 1–6.

32.

Moore

M. G.

Thompson

M. M.

(1990). Effects of distance learning: A summary of literature. American Center for the Study of Distance Education.

33.

Muilenburg

L. Y.

Berge

Z. L.

(2005). Student barriers to online learning: A factor analytic study. Distance Education, 26(1), 29–48.

34.

O’Malley

McCraw

(1999). Students perceptions of distance learning, online-learning and the traditional classroom. Online Journal of Distance Learning Administration, 2(4), 1–10. http://www.westga.edu/~distance/ojdla/winter24/omalley24.pdf

35.

Pant

(2005). Attitude of distance learners towards multimedia approach to instruction. Asian Association of Open Universities Journal, 1(1), 65–72.

36.

Richardson

J. T.

(2009). The attainment and experiences of disabled students in distance education. Distance Education, 30(1), 87–102.

37.

Richardson

J. T.

(2015). Academic attainment in visually impaired students in distance education. British Journal of Visual Impairment, 33(2), 126–137.

38.

Sanders

D. W.

Morrison-Shetlar

A. I.

(2001). Student attitudes toward web-enhanced instruction in an introductory biology course. Journal of Research on Computing in Education, 33(3), 251–262.

39.

Sombrio

Primo

Ulbricht

Villarouco

(2016). Blind students’ learning experiences: Challenges and opportunities in design for inclusion. In Rocha

Á.

Correia

Adeli

Reis

Mendonça Teixeira

(Eds.), New advances in information systems and technologies: Advances in intelligent systems and computing (pp. 1097–1106). Springer.

40.

Tabachnick

B. G.

Fidell

L. S.

(2001). Using multivariate statistics. Allyn and Bacon.

41.

Tekinarslan

(2008). Attitudes of Turkish distance learners toward internet-based learning: An investigation depending on demographical characteristics. Turkish Online Journal of Distance Education, 9(1), 67–84.

42.

Thompson

H.-Y.

(2005). Chinese graduate students’ experiences and attitudes toward online learning. Educational Media International, 42(1), 33–47.