The practice of using images in teaching is widespread, and is supported by the “multimedia principle” that people learn better from words and images than from words alone . Images can also communicate complex relationships among conceptual components more efficiently than words alone . In science education, images are used so extensively that some have argued they are now the “main vehicle of communication” , and this phenomenon is especially notable in the field of biochemistry where abstract concepts are communicated using an enormous variety of diagrammatic images, models, and maps, both for communication among experts and between instructors and students . Indeed, Habraken  argues that in the last twenty years chemistry and biochemistry have moved from being mathematical-logical to visual-logical sciences.
Biochemistry textbooks include thousands of colorful images, ranging from photographic/realistic to abstract/schematic  and an online search for images of any biochemical concept produces thousands of images in a fraction of a second, with multiple ways of representing the same thing. There are countless software options for creating molecular models, diagrams, images, and schemes  and as a result of this students of biochemistry in classrooms around the world are presented with an extraordinary range of images to support their learning.
Despite enthusiastic use of images in biochemistry classrooms, our own classrooms at the University of Alberta being no exception, we know little about the effectiveness of our pedagogical approach. We have little substantive information about how specific images work in producing “useful mental models” [6-9] and it has been argued that graphics are not always appropriate and could actually interfere with development of mental models . Even in sciences with well-defined visual “conventions” such as chemistry and physics, the interpretation of images is known to be cognitively demanding, leading to misinterpretation and incorrect reasoning .
In studies with biochemical images specifically, students have demonstrated a variety of difficulties in interpretation, especially of the more “simple” schematic diagrams and particularly in the absence of prior conceptual knowledge [13-15]. These findings are in accordance with other work suggesting that the interpretation of schematic scientific diagrams, in general, requires prior knowledge of the concept [16-18]. It has been suggested that stylized or schematic diagrams may not always facilitate the development of appropriate mental models in learners, because the model often remains at the perceptual level. That is, students describe biochemical concepts in terms of the image, referring to features of the image such as “the blue circle” or “the red triangle” . Lastly, but importantly, much evidence suggests that learner differences, including visual cognitive skills, are important in learning with images, both generally  and for biochemical diagrams in particular .
The evidence discussed above suggests that selecting the appropriate quantity, style, and quality of visual material to support learning in biochemistry is at best complex and at worst impossible! However, it is important to remember that the images we use are only one part of a complex pedagogical approach. In the classroom we do not communicate concepts using images alone, but rather using a complex combination of both words and images, and we know little about the importance of the images we use in helping students to understand the concepts presented. We also know little about individual differences which might affect learning in a typical biochemistry classroom. In this context, the primary objective of the study reported here was to explore the importance of images as a learning tool in an introductory biochemistry class. To begin this exploration, we developed a novel scale to assess students' attitude toward the images used. An open-ended question was included in the survey, to explore in greater depth the students' perception of the role and importance of images in the development of their own conceptual understanding. We then investigated possible relationships between learner differences and learning outcomes in the course. The first two learner differences, attitude toward biochemical images and visual cognitive skills , were selected based on their possible importance for learning in a course which involves the extensive use of images. The third learner difference, learning approach , was chosen as a relevant learner “characteristic” because research into university learning and teaching has suggested that approach is an important factor in learning outcomes  which is affected by teaching practices .
Participants were recruited from a Spring session introductory biochemistry course (BIOCH 200) at the University of Alberta in Canada. This course ran for approximately 6 weeks, from early May. Fifty-three students completed the course but the number of students who participated in the two surveys differed slightly, depending on when the survey was administered and whether the individual was willing to complete the survey atthat time. Forty-eight students completed the Visual Learning in Biochemistry survey and 45 completed the Learning Approaches survey (although two of these omitted responses to items on the deep learning scale). A subgroup of volunteers was recruited for the Ekstrom tests of visual cognitive skills because these tests required a specific and additional time commitment from the participant. These individuals were recruited through email messages which were sent to all those registered in the class. Experience with the images used in an introductory biochemistry course (BIOCH 200) and willingness to volunteer were the only selection criteria. Ultimately, 15 students who completed the course took part in the Ekstrom tests and all of these individuals responded to both surveys.
Introductory Biochemistry (BIOCH 200) at the University of Alberta is taught in large-enrollment classes with multiple, parallel sections. Importantly, the students who take this course are mostly “non-majors” who are registered in a variety of programs of study, including nutrition, kinesiology, dental hygiene, medical laboratory sciences, chemistry, engineering, and general sciences. They have diverse interests and motivations, and they differ notably in their attitude toward the subject matter (unpublished observations). Overall, the student demographic in BIOCH 200 is mixed, with very few students enrolled in the biochemistry program itself (<1%).
Administration of Surveys
Two surveys were used in this study; the Learning Approaches survey and the Visual Learning in Biochemistry survey. On both surveys, participants were asked to respond to statements using a Likert scale. Responses to items were collected with five options, strongly agree (SA), agree (A), neutral (N), disagree (D), and strongly disagree (SD). The surveys were handed out at the beginning of a class, in Week 4 (Learning Approaches) and week 5 (Visual Learning in Biochemistry). Participants completed the surveys at their own pace, returning them to the researcher before the instructor arrived. Students who were not present and students who did not wish to participate did not complete the survey. The Visual Learning in Biochemistry survey was administered toward the end of the course so that the students had already worked with a range of the images and had experience in viewing and studying with them.
The Ekstrom Tests of Visual Cognitive Skills
Ekstrom tests are factor-referenced cognitive tests which have extensive research into their construct validity . The five tests selected for use in this study are listed and their factors defined in Table 1. The tests were chosen from a range of 72 marker tests for 23 cognitive aptitude factors. For this study, the tests selected were intended to assess a range of the visual cognitive skills which might play a role in enabling students to “read” and interpret the images used in class more easily. Specifically, the factors tested here were flexibility of closure, speed of closure, perceptual speed, and spatial orientation. This is by no means an exhaustive list of the aptitude factors which might be involved in interpretation of biochemical images. For example, no tests for visualization, figural flexibility, or flexibility of use were included here.
Table 1. The Ekstrom tests of visual cognitive skills
Definition of the factor
Hidden figures (CF-1)
Flexibility of closure
“The ability to hold a given visual percept or configuration in mind so as to disembed it from other well defined perceptual material.”
Gestalt completion (CS-1)
Speed of closure
“The ability to unite an apparently disparate perceptual field into a single concept.”
Identical pictures (P3)
“Speed in comparing figures or symbols, scanning to find figures or symbols…”
Card rotation (S1)
Two measures of “the ability to perceive spatial patterns or to maintain orientation with respect to objects in space.”
Cube comparisons (S2)
(Note: The tests described for this factor have not always described a single tight factor and they could be considered representative of subfactors.)
To ensure their validity in this study, the five tests were administered and scored exactly as recommended in the “Manual for Kit of Factor-Referenced Cognitive Tests” . The tests were carried out in the student services office at a time convenient for the participant, and the tests were administered in the same order for each participant.
The Learning Approaches Survey: Scale Analysis and Calculation of Scores
The Learning Approaches survey was intended to assess the participants' overall approach to learning, based on the ASSIST for Students . Six items were selected from the inventory for each of the three major approaches to learning, namely deep (items 1–6), strategic (items 7–12), and surface or apathetic (items 13–18). In each case, the six items were selected from at least two of the related subscales associated with that approach. The items used are listed in Table 2.
Table 2. Items used on the Learning Approaches survey
aItems were selected from the Approaches and Study Skills Inventory for Students (ASSIST) . Six items were selected from at least two of the related subscales for each of the three major approaches to learning, deep (Items 1–6), strategic (Items 7–12), and surface or apathetic (Items 13–18).
1. When I am reading, I stop from time to time to reflect on what I am trying to learn.
2. I try to relate ideas I come across in this course to other topics or courses whenever possible.
3. Often I find myself questioning things I hear in the lectures.
4. It's important for me to be able to follow the argument or see the reason behind things.
5. I usually set out to understand for myself the meaning of what we have to learn.
6. When I'm working on a new topic I try to see in my own mind how all the ideas fit together.
7. I keep an eye open for what lecturers seem to think is important and concentrate on that.
8. I'm good at following up on reading suggested by the lecturers.
9. I think I'm quite systematic and organized when it comes to revising for exams.
10. I work steadily throughout the course rather than leave it all to the last minute.
11. I organize my study time carefully to make the best use of it.
12. When working on practice problems I make sure I understand the reasoning behind the correct responses.
13. I tend to read very little beyond what is required to pass.
14. I gear my studying closely to what is required for the exams.
15. I sometimes find it hard to understand the large volume of content we are presented with.
16. I find I have to concentrate on just memorizing most of this material.
17. I'm not really sure what's important in lectures so I just try to get down everything I can.
18. I don't find much of the course content interesting or relevant to me.
Since the items on the learning approaches survey were intended to assess the three distinct approaches (deep, strategic, and surface), the data were subjected to principle component analysis (data not shown). The Kaiser–Mayer–Olkin measure of sampling adequacy (KMO = 0.473) was below the minimum acceptable. There were also 64% non-redundant residuals with absolute values greater than 0.05. However, Bartlett's test of sphericity (χ2 (153) = 323.178, p < 0.001) indicated that inter-item correlations were large enough for principle component analysis. The eigenvalues and a scree plot supported the extraction of three components which, cumulatively, explained 47.3% of variance. Rotation (Varimax with Kaiser normalization) loaded six items onto each component with factor loadings >0.4. At least four of the items on each component had factor loadings >0.6, suggesting that the component was reliable . The items which loaded onto each component closely matched the distributions described by Entwistle et al. (2000), for deep (Component 1), strategic (Component 2), and surface apathetic (Component 3) approaches to learning . Each of the components identified was thus analyzed as a separate scale.
Deep Learning Approach
Items 1–6 loaded onto Component 1, Cronbach's α = 0.752, and all corrected item-total correlations were greater than 0.3. Strategic Learning Approach: Items 7–11 and 14, loaded onto Component 2. However, the corrected item-total correlation for Item 14 was only 0.291, so it was dropped. For Items 7–11, Cronbach's α = 0.747 and all corrected item-total correlations were >0.3. (In this study, Item 12 did not have a substantive loading on Component 2, possibly because it was taken from a related sub-scale rather than from one of the three subscales which Entwistle states “can be combined with confidence.”) Therefore, for the current study, participants' strategic learning approach was assessed using the five-item scale. Surface Learning Approach: Of Items 13–18, only Item 14 did not have a substantive factor loading on Component 3. For Items 13 and 15–18, Cronbach's α = 0.701, and all corrected item-total correlations were greater than 0.3.
Responses to items were collected using a Likert scale. All of the items were scored strongly agree = 5, agree = 4, neutral = 3, disagree = 2, and strongly disagree = 1. For each participant, a mean item score was calculated for their deep, strategic, and surface learning approach. The range of possible scores is 1.0–5.0, with a higher mean item score representing a greater reported tendency to enact that particular approach to learning. To account for individual differences in scoring behavior, the difference between the mean item score on the deep learning scale and the mean item score on the surface learning scale was also calculated (DEEPSURFACEDIFF). The magnitude of this difference represents the extent to which a participant enacts predominantly deep learning strategies compared with surface learning strategies. Given the scale used, the range of possible scores is +4.0 (predominantly deep) to −4.0 (predominantly surface) and a score of 0.0 represents a balance in the reported use of deep and surface strategies.
The Visual Learning in Biochemistry Survey
The Visual Learning in Biochemistry survey was intended for the development of a novel scale to assess students' self-reported attitude toward images in a biochemistry class. The items tested are shown in Table 3. These items were designed to explore participants' general attitude toward images of biochemical concepts as well as their self-perceived ability to use images of biochemical concepts to support their learning. Statements were written with extensive reference to numerous attitude surveys and questionnaires reported in the literature or available online . The statistical analysis and revision of the scale to assess the students' attitude toward images is described in the results section, as it comprises a major portion of the work reported here.
Table 3. Items tested on the Visual Learning in Biochemistry questionnaire
aThe items marked * (7, 9, 12, 14, 16, 17, 18, 19, 20, and 21) were either reverse-phrased or described a less positive attitude toward images (*) and so they were reverse scored.
1. I find the illustrations and animations used in biochemistry classes very useful.
2. When I look at a figure the instructor showed in class I remember the figure but not what the instructor said about it.
3. When someone asks me about biochemistry, the first thing that comes to mind is the illustration used in class or the book, and then I can find words to describe what I see.
4. When I think about a concept in biochemistry an image, model or map appears in my mind first.
5. The figures used in biochemistry text books and classes usually make sense to me immediately.
6. I like the use of figures to illustrate biochemistry concepts.
7. I understand biochemical concepts best by reading printed materials or listening to the lecturer.*
8. If I try to explain a concept to a friend, it helps me to try to draw it out.
9. I often find myself looking at the diagrams and representations used in biochemistry and feeling that they mean nothing to me.*
10. For me, a graph showing the oxygen binding behavior of hemoglobin, as it changes with partial pressure of oxygen, describes the changes clearly and completely.
11. When studying biochemistry, I often draw diagrams for myself.
12. If I am confused about a concept in biochemistry, I will read the text to clarify the concept.*
13. If I look at a biochemical illustration for long enough, I can fully understand the concept it represents.
14. The pictures and diagrams used in lectures are useful additions to what the lecturer says, but the verbal explanations are more important for me.*
15. Once a diagram or representation has been explained to me, I “see” the concept in my head using that diagram.
16. Biochemical diagrams are often quite confusing and don't match what I imagine in my head.*
17. I am more likely to understand if someone explains a concept verbally than I am if I try to interpret a diagram.*
18. I prefer the slides with words on them instead of pictures.*
19. I find it difficult to “translate” diagrams quickly to determine their meaning during class.*
20. If I draw a diagram representing my understanding of a biochemical concept, it often looks very different from the diagrams I find in text books.*
21. I don't like the diagrams in text books of biochemistry.*
22. I love all the colorful figures and models in biochemistry text books.
In addition to the numerically scored items, an open-ended question was included on the survey, as follows: “Please use the space below (and overleaf if you would like) to expand on any of your responses, above. I am particularly interested in how effective the images and diagrams used in biochemistry are in helping YOU to understand the concepts, both during class and as you study.”
All statistical analyses were performed using SPSS. Alpha was set at 0.05.
Prior to data collection, the procedures for this research project were subject to review and approval by the University of Alberta Ethics Review Board. Guidelines for ethical research practices from the University of Alberta (University of Alberta Standards for the Protection of Human Research Participants) were consulted and followed. At all stages in the research process, the researcher remained aware of the ethical obligations to the research participants, which include voluntary and informed consent, the avoidance of deception, respect for the right to withdraw, and privacy.
Development of a Scale to Assess Learners' Attitude Toward Images
The 22 items tested on the Visual Learning in Biochemistry questionnaire are shown in Table 3. Responses were collected using a five-point Likert scale with the following scoring: strongly agree = 5, agree = 4, neutral = 3, disagree = 2, strongly disagree = 1, such that a larger score indicated a more positive or favourable attitude toward using and learning with images. Reverse-phrased items and items which described a less positive attitude toward images (*) were reverse scored. Fifty-three completed surveys were received and the responses were analyzed using SPSS, as described below.
Initial Scale Reliability and Principle Component Analysis
Initial scale reliability analysis showed that Cronbach's α = 0.835. However, values this high may simply reflect a relatively large number of items (Cortina, 1993) rather than indicating that the scale is uni-dimensional or that all the items “fit.” Indeed, item analysis showed that the corrected item-total correlations for items 5, 10, and 12 were very low. These items also had few inter-item correlations greater than 0.3, and so they were removed prior to principle component analysis. The remaining 19 items were subjected to an exploratory principle component analysis. The KMO measure of sampling adequacy (KMO = 0.751) was good, and KMO values for individual items were all above the acceptable limit of 0.5. The lowest value was 0.529, and all others were > 0.63. Bartlett's test of sphericity (χ2 (171) = 409.638, p < 0.001) indicated that inter-item correlations were large enough for principle component analysis.
Principle component analysis identified five components in the data with eigenvalues greater than Kaiser's criterion of 1. In combination, these components explained 64.69% of variance. Since Kaiser's criterion may over-estimate the number of factors to extract, a scree plot of the eigenvalues was produced (data not shown). This plot suggested that either two or four factors should be considered. Given these different criteria for selecting the appropriate number of factors, the 5, 4, and 2 component models were compared. After the five component extraction, communalities ranged from 0.483 to 0.838, with 15 items scoring > 0.6. When two components were extracted, only two items had communalities > 0.6, whereas when four components were extracted this increased to nine items. The fit of the three models was also evaluated by comparing their non-redundant residuals, where if the proportion exceeds 50% the model may give “cause for concern” . The five component model had 42% non-redundant residuals, the four component model had 47%, and the two component model had 58%.
After consideration of the data discussed above the four component model was selected, and an oblique rotation (direct oblimin) was chosen to allow for correlation between components. (It was assumed that if distinct components existed within the attitude scale, they were likely to be related to one another.) Ultimately, the component correlation matrix confirmed that Components 1, 3, and 4 were not independent and so an oblique rotation was appropriate. The results of the principle component analysis are summarized in Table 4. For interpretation, items were sorted by the magnitude of the factor loadings. Factor loadings >0.5 (shown in bold) were considered to have substantive importance .
Table 4. Exploratory principal component analysis of data from the Visual Learning in Biochemistry questionnaire
Rotated factor loadings
aExtraction method: Principal component analysis. Rotation Method: Oblimin with Kaiser normalization. Rotation converged in 19 iterations. Items are sorted by the magnitude of the factor loadings. Factor loadings > 0.50, which are considered to be substantive, appear in bold. N = 53 completed questionnaires.
21 I don't like the diagrams in text books of biochemistry.
6 I like the use of figures to illustrate biochemistry concepts.
8 If I try to explain a concept to a friend, it helps me to try to draw it out.
1 I find the illustrations and animations used in biochemistry classes very useful.
22 I love all the colourful figures and models in biochemistry text books.
9 I often find myself looking at the diagrams and representations used in biochemistry and feeling that they mean nothing to me.
20 If I draw a diagram representing my understanding of a biochemical concept, it often looks very different from the diagrams I find in text books.
2 When I look at a figure the instructor showed in class I remember the figure but not what the instructor said about it.
16 Biochemical diagrams are often quite confusing and don't match what I imagine in my head.
19 I find it difficult to “translate” diagrams quickly to determine their meaning during class.
11 When studying biochemistry, I often draw diagrams for myself.
14 The pictures and diagrams used in lectures are useful additions to what the lecturer says, but the verbal explanations are more important for me.
7 I understand biochemical concepts best by reading printed materials or listening to the lecturer.
17 I am more likely to understand if someone explains a concept verbally than I am if I try to interpret a diagram.
18 I prefer the slides with words on them instead of pictures.
3 When someone asks me about biochemistry, the first thing that comes to mind is the illustration used in class or the book, and then I can find words to describe what I see.
15 Once a diagram or representation has been explained to me, I “see” the concept in my head using that diagram.
4 When I think about a concept in biochemistry an image, model or map appears in my mind first.
13 If I look at a biochemical illustration for long enough, I can fully understand the concept it represents.
Reliability of Subscales
The reliability of each component subscale identified in the principle component analysis described above is reported (Cronbach's α) at the bottom of Table 4. The value of Cronbach's α for three of the components (1, 3, and 4) was > 0.7 and all items had corrected item-total correlations > 0.3. The remaining component (2) had an extremely low value for α and very poor corrected item–total correlations. After interpreting their content it was decided that these items simply did not “fit” to create a useable subscale. This conclusion was supported by the component correlation matrix (data not shown) which indicated that Component 2 had little if any relationship with the other components. The items which loaded onto Component 2 were thus not considered further.
The three components, or subscales, identified above (Table 4) were evaluated to see if any could reasonably be combined to make a single scale. Component 1 contains items which, overall, describe whether the respondent generally likes the images used in biochemistry classes and text books, and the extent to which they consider them useful or helpful as illustrations of the concepts. Component 4 contains items which address the participants' self-reported tendency and comfort in “thinking” about biochemical concepts in terms of the images presented. Since the correlation between mean item scores on Components 1 and 4 was substantive (r = 0.516, p < 0.001, n = 53), and the items all seemed to address students' general attitudes toward biochemical images used in the classroom, the items were combined to create a single, nine-item scale which is referred to as the Attitude Towards Images scale (Cronbach's α = 0.819; all corrected item-total correlations > 0.450). Each participant's mean item score on this scale was calculated, with a larger mean item score indicating greater agreement with the statements on the scale (5 = strongly agree). The mean item scores were interpreted here as representing the extent to which our students liked the biochemical images in class, found them helpful, and felt prepared or able to use them to facilitate their learning.
The items on Component 3 (7, 14, and 17) did not seem to fit on the Attitude Towards Images scale. Rather, these items seem to address a possible preference for words (verbal and written explanations) rather than images. Since they arguably fit together as a small but distinct subscale (Cronbach's α = 0.730), a separate mean item score was calculated for these three items. These items had been reverse-scored in the initial analysis (because it was incorrectly assumed that agreement with these statements represented a negative attitude toward images) and so prior to calculation of the mean item score the individual scores were reversed, such that a larger mean item score indicates greater agreement with the statements (5 = strongly agree). This mean item score was interpreted as representing the participants' preference for verbal or written explanations rather than images, and is referred to as the Preference for Words subscale.
Attitude Toward Images: The Distribution of Mean Item Scores
As described above, mean item scores on the Attitude Towards Images scale and on the Preference for Words subscale were calculated for each participant. In both cases, the minimum score possible was 1.0 (strongly disagree) and the maximum score possible was 5.0 (strongly agree). As commonly observed when using Likert scales, the distribution of scores was non-normal (data not shown).
The distributions of the mean item scores are similar (Fig. 1) with the most notable difference being that the range of scores is somewhat greater on the Preference for Words subscale (2.00–5.00) than on the Attitude Towards Images scale (2.56–4.67). On the Attitude Towards Images scale, 75% of the participants had a mean item score ≥3.33 and 50% had a mean item score ≥3.78. Similarly, on the Preference for Words subscale, 75% of the participants had a mean item score ≥3.00 and 50% had a mean item score ≥3.67. That is, a large majority of the participants expressed a positive attitude toward the images, generally agreeing that the images were useful learning tools. At the same time, the participants tended to agree that verbal and written explanations were more important to them than images in helping them to understand the concepts.
Attitude Toward Images: Qualitative Feedback
Participants were invited to respond to the following statement on the survey: Please use the space below (and overleaf if you would like) to expand on any of your responses, above. I am particularly interested in how effective the images and diagrams used in biochemistry are in helping YOU to understand the concepts, both during class and as you study.
Written responses to this question were received from 19 of the 48 individuals who completed this survey, and these are presented, unedited, in Table 5. The responses were read several times and categorized. Overall, the comments appear to address two major themes: firstly, the respondents seemed to feel that the use of images in class was helpful, although they noted that some images were difficult to interpret; secondly, the respondents expressed quite consistently the opinion that images alone are not effective in helping them to understand concepts, that words are needed, sometimes before the diagram or representation is viewed. For example, SA stated that “I like representing concepts with diagrams only after I have a good understanding of the material. I need to learn (read) about the concept first and only then do diagrams help me.” SB's comment summarizes the sentiments generally expressed by the respondents in this study: “I find that even though the diagrams are in themselves helpful in explaining concepts, having a lecturer elaborate on the diagram and point out all the subtle details and link it to other concepts is more useful/helpful than either just having the diagram or just having the concept told to us.”
Table 5. Responses to the open-ended question
aParticipants were invited to respond to the following open-ended question on the Visual Learning in Biochemistry survey. “Please use the space below (and overleaf if you would like) to expand on any of your responses, above. I am particularly interested in how effective the images and diagrams used in biochemistry are in helping YOU to understand the concepts, both during class and as you study.” The responses are presented here exactly as they were written by the participants.
WS: They don't play a big role in my understanding of biochemistry. I would rather read about the concept than spend way longer trying to understand a picture + interpret it.
SM: Images and diagrams are important during class. I often will re-draw important images myself and this helps me memorize the material.
LZ: It depends. Some figures and drawings are terrible. I find it very hard to understand how a protein works without an explanation of the active site and its features. Showing a ‘blob’ of substrate on a larger ‘blob’ of protein means nothing to me except size comparison.
AS: I do draw out concepts and make my own charts and mindmaps while studying for biochemistry. If reading a procedure I prefer a drawing of how the process is done instead of words describing the step-by-step process. However, I dislike diagrams that have no descriptions below it or if the instructor did not explain what is going on in the diagram as it confuses me.
JT: For me, I find that images and diagrams are useful in regards of summarizing and explaining concepts that are to be learned in any class. At times depending on the given information or diagram, I would prefer one over the other depending on how well I comprehend the information being presented. I find that images and diagrams are both effective in the learning of biochemistry, as they both provide an alternative in regards to understanding the material.
WR: I understand concepts more properly when they are written or someone verbally tells me rather than drawing it out. But if I am required to understand a concept using a diagram I will learn it but always prefer written notes on the side of the diagram.
SB: Images and diagrams are helpful but I need a good explanation with it as well, in words.
SP: Images and diagrams are pretty effect to help me understand. It makes it easier to understand concepts.
VB: I wouldn't understand 80% without it. Only diagrams that do not make sense are the one that do not have enough information provided (concept that we have to accept without explanation or there is not enough time in the class to explain it).
LM: Lots of diagrams followed by thorough explanations of them is really helpful. This way, when going back to study I can remember what was said by using the diagram.
DW: I feel as though the diagrams are very effective deffinitely [sic] help a lot.
CS: I find the explanations are what I think of when I see the image.
DG: I like using images they help me understand it better, but it works best for me if the teacher goes over the image so it can help me understand exactly what I am seeing, which helps me remember the concept better.
TH: I don't find pictures very useful. I am more interested in writing out the balanced equation or structure of an amino acid. If I can see patterns in the structure it is far easier to remember.
DP: I find that images are VERY helpful and often if the images, diagrams or textbook notes are not enough to fully understand a concept I watch video's online. Moving images with explanations I find are most helpful above all.
SA: I like representing concepts with diagrams only after I have a good understanding of the material. I need to learn (read) about the concept first and only then do diagrams help me. I find it difficult to grasp new material from a diagram alone. However, they often help “polish off” my understanding before an exam.
UM: The images only help when the lecturer explains them. Often, if the lecturer doesn't explain the image, I don't fully understand the concept.
SB: I find that even though the diagrams are in themselves helpful in explaining concepts, having a lecturer elaborate on the diagram and point out all the subtle details and link it to other concepts is more useful/helpful than either just having the diagram or just having the concept told to us.
Ekstrom Tests of Visual Cognitive Skills
The Ekstrom test data shown in Table 6 were obtained from 15 of the 53 students enrolled in BIOCH 200. Five tests, which assess the four distinct aptitude factors described in Table 1, were administered. The scores were approximately normally distributed, except for the Gestalt completion test (data not shown), and the values for the mean and standard deviation on each test were comparable to those reported for other groups of college-age students in the Manual for Kit of Factor-Referenced Cognitive Tests .
Table 6. Statistics for scores on the Ekstrom tests of visual cognitive skills
Cube comparisons test (42)
Card rotations test (160)
Identical pictures test (96)
Gestalt completion test (20)
Hidden figures test part I (16)
aThe five tests selected and their factors are defined in Table 1. The tests were administered in the same order for each participant and scored exactly as recommended in the “Manual for Kit of Factor-Referenced Cognitive Tests” . The maximum possible score for each test is indicated in parentheses. N = 15.
Entwistle's Learning Approaches: Deep, Strategic, and Surface
The ASSIST was developed at the Center for Research on Learning and Instruction, University of Edinburgh, Scotland . This instrument classifies approaches as deep, surface, and strategic. A deep learning approach is characterized by subscales assessing the seeking of meaning, relating ideas, and use of evidence. A strategic learning approach is characterized by subscales assessing organization, time management, and meeting assessment requirements. A surface learning approach is characterized by subscales assessing lack of purpose, memorizing, fixation on required content only (termed syllabus-boundness).
Mean item scores on each subscale (deep, strategic, and surface) were calculated, as described in the Methods section. On each subscale, the minimum possible mean item score was 1.0 and the maximum possible mean item score was 5.0. A larger score indicates greater agreement with the statements on that subscale. The range of mean items scores on the subscales was 2.67–5.00 (deep), 2.00–5.00 (strategic), and 1.60–4.00 (surface), with median scores of 3.83 (deep), 3.25 (strategic), and 2.79 (surface) (data not shown).
To account for individual differences in scoring behavior, the difference between mean item score on the deep learning scale and mean item score on the surface learning scale was calculated (DEEPSURFACEDIFF) for each participant. The magnitude of this difference represents the extent to which that participant reports enacting deep learning strategies compared with surface learning strategies. The range of possible scores is +4.0 (predominantly deep) to −4.0 (predominantly surface) where a score of 0.0 represents a balance in the use of deep and surface strategies reported by that individual. In this study, the range of DEEPSURFACEDIFF scores (n = 50) was only −0.53 to 3.20, suggesting that none of the participants believed they enacted surface learning strategies predominantly relative to deep learning strategies. Notably, 50% of the respondents scored between −0.53 and 1.10, suggesting that a large number had reported enacting deep and surface strategies approximately equally.
For this study, “learning outcome” was defined as the participant's cumulative score on three multiple choice examinations. The distribution of cumulative scores was approximately normal (W(71) = 0.098, p > 0.05). Table 7 shows correlations between learning outcome and the other variables, using either Pearson's correlation coefficient (if both sets of scores were approximately normally distributed) or Spearman's rho. As shown, there was no correlation between learning outcome and score on any of the five tests of visual cognitive skills. There was also no correlation between learning outcome and mean item score on the Attitude Towards Images scale or on the Preference for Words subscale.
Table 7. Visual cognitive skills, attitude toward images, and learning approach: Correlations with learning outcomes
aLearning outcome was defined as the participant's cumulative score on three multiple choice examinations. Mean item scores on the revised Attitude Towards Images scale, on the Preference for Words subscale, and on the deep, strategic, and surface learning approaches subscales, were calculated for each participant. In all cases a larger mean item score indicated greater overall agreement with the statements on the scale. DEEPSURFACEDIFF is the difference between the participant's mean item score on the deep learning scale and mean item score on the surface learning scale. The range of possible scores is +4.0 (predominantly deep) to −4.0 (predominantly surface).
Hidden figures test (Score)
Identical pictures test (Score)
Card rotations test (Score)
Cube comparisons test (Score)
Gestalt completion test (Score)
Correlation coefficient (rs)
Attitude toward images (Mean item score)
Correlation coefficient (rs)
Preference for words (Mean item score)
Correlation coefficient (rs)
Deep learning (Mean item score)
Correlation coefficient (rs)
Strategic learning (Mean item score)
Correlation coefficient (rs)
Surface learning (Mean item score)
Correlation coefficient (rs)
Correlation coefficient (rs)
In contrast, and perhaps not unexpectedly, learning outcomes were correlated with the learner's reported approach to learning. Specifically, students who reported a relatively dominant deep learning approach (a greater DEEPSURFACEDIFF score) were more likely to obtain a greater overall percentage score in BIOCH 200 (r = 0.420, p = 0.003, n = 45), and students who scored higher on the surface learning scale were notably less likely to perform well in examinations (r = −0.479, p < 0.001, n = 45).
Development of a Scale to Assess Attitude Toward Images
In this study, a scale was developed for use as a tool to assess students' attitude toward the use of images in a biochemistry class. This is a new scale, nothing similar having been reported in the literature. Items on the scale were intended to assess the students' personal sense of “visual literacy,” a construct which has been defined as the “inclination and ability to attend to and process graphic information” . It was considered important feedback to inform course improvement activities in a class in which images are presented routinely and in large numbers, comprising a significant portion of the learning “tools” provided to the students.
The development of a good scale is a lengthy process  and so the scale described here will be subject to revisions. However, in this study, principle component analysis and reliability testing of responses to the 22 items included in the original questionnaire resulted in the construction of a nine-item scale, referred to as theAttitude Towards Images scale. This scale was statistically reliable and can thus be considered a reasonable measure of the “attitude” of the students in this particular study, keeping in mind that scales such as this do not produce an absolute measure.
Statistically, the Attitude Towards Images scale was found to be reasonably reliable. However, it is difficult to find external criteria with which to determine the validity of an attitude scale . Therefore, this scale is considered to have only a content validity defined by the items it contained. This was an important consideration in the decision to combine the two related subscales (Components 1 and 4) to create a broader, single scale. In the future, additional efforts will be made to assess the validity of the scale, especially with respect to the respondents' ability to interpret graphic information. This, however, is a complex undertaking beyond the scope of the current study, and so it is noted that the scale used here was intended to assess, in part, the respondents' self-reported ability to “read” graphical information and not their measured ability to do so.
Students' Attitude Toward Images
Based on the distribution of mean item scores on the Attitude Towards Images scale, it is suggested that a large majority of the participants in this study had a positive attitude toward the images used in the class. In general, the participants agreed that they liked the images, found them helpful, and were willing and able to interpret them; that is, they perceived themselves as “visually literate” . This interpretation of the mean item scores is supported by the responses to the open-ended question on the survey, in which the respondents expressed strongly that they found the images helpful in illustrating and summarizing concepts. Given the extent to which images are presented as a learning tool in our introductory biochemistry classes, this is important feedback. It is also in keeping with the dual coding theory, which states that the chances of encoding new knowledge in long-term memory are increased by using both text and image formats because two memory traces are formed . Images may also be helpful for students as they communicate complex relationships among conceptual components efficiently . Feedback to the open-ended question in this study supported this suggestion, with several of the students reporting that they found the images useful in summarizing concepts.
It is important to note that the distribution of mean item scores on the Attitude Towards Images scale assesses the participant's self-perceived visual literacy, and provides no information about the effectiveness of individual images in the development of accurate mental models. However, in this study, the intention was not to measure the effectiveness of specific images, but rather to explore the students' attitude toward the use of images in a biochemistry classroom, to develop an understanding of how our learners use the images as learning tools and of the importance they place on the images in the development of their own understanding of abstract concepts.
Words are Important
Importantly, whilst the students generally reported a positive attitude toward images, many also expressed the sentiment that words played a more important role in the development of their conceptual understanding than did images. This sentiment was expressed in the distribution of mean item scores on the short Preference for Words subscale which was identified during principle component analysis. Notably, three quarters of the respondents scored in the neutral to strongly agree range on this scale. It also emerged clearly in the written responses to the open-ended question on the survey. This question asked participants to describe how effective the images were in helping them to understand the concepts, and almost all of the 19 respondents clearly stated that whilst the images were helpful, they were only helpful in conjunction with words, either spoken by the lecturer or written clearly with the diagram. Several participants even noted that they required words prior to the use of an image. This is in keeping with the findings of other investigators who reported that students were more likely to have difficulty interpreting schematic biochemical images when they had low prior conceptual knowledge [13-15]. In this study, the students' written comments suggested clearly that they were aware of the difficulty of interpreting diagrams prior to having the concept explained to them.
Learner Differences in Attitude Toward Images and Preference for Words
The majority of students in this study reported both a positive attitude toward images and a preference for words rather than images to enable them to understand the concepts presented. However, on both scales approximately one-quarter of the respondents scored in the disagree range. This disagreement likely reflects differences in visual and verbal preferences among the participants, with some participants expressing a strong preference for either images or words, and others expressing a more balanced preference. This suggestion is supported by the observation that there was no correlation between mean item scores on the two scales (data not shown). In both cases, the precise meaning of the disagreement requires further exploration. For example, a more negative attitude toward images might simply reflect a strong preference for words, and vice versa. Alternatively, it might indicate that the respondent disliked certain images or the manner in which they were used.
Visual Cognitive Skills and Learning Outcomes
Given the extensive use of images used in our introductory biochemistry course, it was proposed that either self-reported attitude toward images or visual cognitive skills might be related to learning outcomes, especially since visual cognitive skills have been shown to be important in the interpretation of specific images and especially of highly schematic diagrams . Learning outcomes were defined in this study as the participant's cumulative score on three multiple choice examinations which include questions from a large database with known and varying degrees of difficulty, which assess multiple learning domains in Bloom's taxonomy . Interestingly, there was no correlation between either visual cognitive skills or attitude toward images and learning outcome. There are many possible reasons for this, the most notable being that while images are used extensively in the classroom their role in communication of concepts is not central but supportive, and that they are most useful for students with prior conceptual understanding. As suggested by the students in this study, it is possible that in most cases the images used in class function as illustrations and summaries of concepts which must have been explained by the instructor. Thus, there is no necessary relationship between learning outcomes and visual cognitive skills. That said, in this study only five tests of visual cognitive skills were used and the sample size was small, so it cannot be concluded that visual cognitive skills play no role in learning outcomes.
Attitude Toward Images and Learning Outcomes
With respect to attitude toward images, the scale was intended to assess the students' self-reported willingness and ability to use the images presented as learning tools. As discussed above, the attitude scores are a self-assessment which reflect a learning preference or style rather than a measured ability. There is considerable controversy in the literature regarding the importance of these types of difference in learning. Some researchers argue that rigorously conducted research has struggled to demonstrate that matching instruction with learning style has a significant effect on learning . Others argue that if cognitive styles are measured appropriately, matching instructional materials to the learner's cognitive style can improve learning outcomes in certain circumstances .
The observation that attitude toward images, a learning preference, was not correlated with learning outcomes, is in keeping with these arguments. Other factors may also contribute, however. For example, the distribution of mean item scores for attitude toward images was narrow, with the majority in the agree range and almost none disagreeing. That is, there was no substantive difference among the participants in their attitude toward images. It is also not possible to meaningfully interpret the relative strength of the students' reported attitude because of individual variations in scoring behavior when using a Likert scale.
Learning Approach and Learning Outcomes
While it is argued that “learning styles” may not influence learning outcomes substantially, there are many other learner differences which are considered important in learning outcomes . Among these, learning approach is known to be a context-sensitive learner difference which has a substantive effect on learning . There is a well-tested and reliable scale available for measuring learning approach, the ASSIST for Students , which was employed in this study. The factor structure of the scale was confirmed by principle component analysis and each of the components had good reliability. The factor structure of ASSIST is clear-cut, such that Entwistle argues that these factors provide “well-established analytic categories for describing general tendencies in studying…” . In this study, there was a moderate and significant correlation between learning approach and learning outcomes. Specifically, those students who reported a relatively deeper learning approach were more likely to perform well in examinations, and those students who reported a greater surface approach were less likely to perform well in examinations.
In this study, a novel scale was developed to assess student attitude toward the images used in an introductory biochemistry class that is taken as a requirement by students registered in various distinct programs of study. Overall, the participants (48 of the 53 students registered) expressed a positive attitude toward the images, indicating that they generally liked them and were willing and able to use them as learning tools. However, a majority of the participants also reported that verbal explanations were more important than images in helping them to understand the concepts presented, and neither attitude toward images nor visual cognitive skills was correlated with learning outcomes. Although it is not possible to generalize the findings of this small-scale study beyond the students who participated, the data presented suggest that images were not the “main vehicle of communication” in this biochemistry classroom. Importantly, of the three learner differences assessed in this study, only the students' approach to learning was correlated with learning outcomes. Thus, as we work to improve our teaching practices, it seems that the quality of verbal explanations and of efforts to encourage a deep learning approach may be more important than our choice of images.