The Use of Augmented 3D Holographic Technology in Higher ...

10 downloads 92 Views 1012KB Size Report
analysis of higher education medical students' experiences with the use of the ... holographic technology and its effect on student learning outcome scores.
The Use of Augmented 3D Holographic Technology in Higher Education, Increasing Students’ Learning Outcome Scores: A Mixed Methods Study Sharon Golden PhD Abstract The purpose of the current mixed-method study was to determine whether the use of augmented 3D holograms (treatment) for educational purposes significantly increased students’ learning outcome scores. The quantitative strand investigated whether the use of the treatment in the higher education medical field significantly increased students’ learning outcome scores. Results indicated a slight increase in the adjusted mean for the treatment group’s outcome scores on a posttest compared to the control group, when controlling for the pretest value, but no significant difference was found between the two groups. The qualitative strand included an analysis of higher education medical students’ experiences with the use of the treatment and their perspectives on its use for educational purposes. The results from the qualitative strand helped to explain the results from the quantitative strand, by considering students’ experiences with the treatment. Students expressed their experiences using the treatment, perspectives on the use of the treatment for educational purposes, ease of use, the ability to learn at a more advanced level, and other aspects of the learning experience. The data collected from the qualitative strand indicated that most students in the treatment group found the use of the treatment to be beneficial to their learning. The implications of the current study indicate that most students seek new ways to learn and perceive the treatment as a new learning tool that increases learning. Further implications indicate the implementation of the treatment and the learning environment play an important role in whether the treatment may significantly increase students’ learning outcome scores.

should be focused on are advancing learning environments that drive innovation and increasing collaboration (Johnson, Adams Becker, Estrada, & Freeman, 2015). The current study was a necessary one because augmented 3D holograms can provide a new advanced learning environment that drives innovation and increases collaboration (zSpace, Inc., 2016). Furthermore, this new technology has already made its way into the education system and the business environment. Augmented holographic reality does not fully immerse students into a different environment, but is used to combine the real and virtual worlds (Rizov & Rizova, 2015). To view the augmented 3D hologram the student must wear a pair of 3D holographic glasses that allows him or her to view the real world and virtual elements (3D holograms) at the same time (Rizov & Rizova, 2015). Higher educational institutions need to stay abreast of new technologies by studying the effects that new technologies have on students’ learning processes and students’ learning outcomes. The data collected from studies such as this one will give educational leaders more insight on the future of education and the possibilities that exist using new technology to help increase students’ learning. The current study benefits the instructional design field by providing more insight into the use of augmented 3D holograms as a learning tool for which instruction can be designed. Instruction will need to be designed differently to integrate this new technology so it can be utilized to its fullest capabilities and help to increase student learning. It is suggested that further research is needed to discover the best ways to implement this new technology to help increase students’ learning outcomes.

Keywords Augmented 3D hologram, Augmented holographic reality, zSpace

Introduction There is not enough research on the use of augmented 3D holographic technology and its effect on student learning outcome scores. There has been little research on the use of holograms for educational purposes in the higher education learning environment. The New Medium Consortium (NMC) Report (2015) conducted a study on what kind of technology should be implemented in the higher educational institutions within the next five years and discovered that the two most important areas that

This article can be used for educational purposes only

Methods The framework of this study was built upon constructivism theory, which focuses on experimental learning through real-life experiences (Chmiel, 2014). The use of augmented 3D holograms for educational purposes provides the educator with a teaching tool that is interactive. Students can visualize an object and move it around, change it, and take it apart. This learning environment affords students the opportunity to learn through interactive experiences and collaboration with other students.

1

A Mixed Methods, sequential, explanatory research design was used for the current study. Dr. John W. Creswell is an applied research methodologist at the University of Nebraska and he helped pioneer mixed methods research (Creswell, 2013a). In one of his recent videos Dr. Creswell stated that, "qualitative data gives you the rich detail that no quantitative method will give you" and "qualitative research gives more detail and personal stories about participants' experiences (Creswell, 2013b). The researcher stated that by utilizing a mixed method design, “You have a more complete picture of the understanding of the problem than either qualitative or quantitative by itself would yield" and contends that mixed methods research will be the “dominate methodology” in the future (Creswell, 2013b). The research design for the quantitative strand of the current study consisted of a quasiexperimental, two-group design that compared the adjusted mean of the control and treatment group’s outcome scores, while controlling for the pretest value. A pre-test and posttest was used as well as the random assignment of students to the treatment and control groups using a computer randomization program. The design for the qualitative strand of the current study was a descriptive case study and the focus was on the following processes: Describing the event in rich detail, how it was done and the outcome. The following six previous studies, similar to the current study, where analyzed to compare and contrast procedures implemented and results concluded with the current study: Hackett (2013) titled, “Higher education medical holography for basic anatomy training”; Salvetti and Bertagni (2014) titled, “eREAL: Enhanced reality lab”; Ahmad, Abdullahi, and Usman (2015) titled, “General attitude and acceptance of holography in teaching among lecturers in Nigerian colleges of education”; Ghuloum (2010) titled, “3D hologram technology in learning environment”; National Technology Initiative Leadership Fellows Award winning preliminary paper by Hite and Childers (2016), titled, “Teachers’ pedagogical perceptions of novel 3-D hapticenabled virtual reality technology”; and Tallitsch, Beck, Kelley, and Peters (2012), titled, “The effects of computerassisted instruction in teaching human anatomy: An experimental study”. The current study conducted the qualitative research after the quantitative research was completed.

Population The quantitative strand of the current study took place at a university located in the South Pacific. The qualitative strand of the current study occurred on the computer using Survey Monkey® for the electronic online survey and Skype® software for the online interview. The population of the quantitative strand of the current study consisted of student volunteers who met specific criteria such as, the grade level of first year undergraduate medical student who were enrolled in the subject of osteology. Participants of

This article can be used for educational purposes only

this study were randomly assigned between two groups using students’ identification numbers and a computer randomization program. Seventy students volunteered to take part in the quantitative study and were evenly assigned to the treatment group or the control group. Students in the treatment group used the treatment to study with along with other study methods of their choosing for a 3-week period. Students in the control group were offered the use of the treatment once the investigation was completed. The population for the qualitative strand of the current study consisted of student volunteers from the same population of students who took part in the treatment group during the quantitative strand of the current study. Thirteen student volunteers took part in the online survey and four student volunteers took part in the online interview process.

Treatment The treatment used in the quantitative strand of the current study were augmented 3D holograms. The school where this study took place used Unity® and Maya® software to create their own mockups of parts of the human anatomy. Maya® software allows the user to create their own 3D animations (Autodesk, Inc., 2016). Unity® software is a 3D game development platform (Unity Technologies, 2016). The mockups used in conjunction with the zSpace® system produced the augmented 3D holograms that students in the treatment group used to study with during the quantitative strand of the current study. Student volunteers accessed the augmented 3D holograms through the zSpace® system. The zSpace® system incorporates the Hewlett Packard HPZ Holographic 3D Virtual Reality® monitor, which projects a 3D holographic image. The image is projected from the monitor and can be manipulated using a stylus pen (HP Development Company, L.P., 2016). Using 3D glasses, the image is projected to the person wearing the glasses (HP Development Company, L.P., 2016). The image can also be broadcast onto a larger screen for educational and training purposes (HP Development Company, L.P., 2016). The monitor uses full motion parallax sensors that track and move with the 3D glasses the user wears, which allows the user to see around the image being projected (HP Development Company, L.P., 2016). Using a stylus pen, the user can rotate, manipulate, navigate, and zoom the image in and out (HP Development Company, L.P., 2016). For example, the stylus pen can be used to move a 3D image of a human skeletal bone around, so that all angles of the bone can be viewed, focused on, and studied (HP Development Company, L.P., 2016). The company zSpace® is a technology provider that specializes in interactive hardware and software platforms (zSpace, Inc., 2016). Products by zSpace® are used to allow students to interact with augmented reality and holograms, which is accomplished using augmented 3D holographic imagery (zSpace, Inc., 2016). The zSpace®

2

system uses the Hewlett Packard HPZ holographic 3D virtual reality® monitor, which is an integral part of the system (zSpace, Inc., 2016).

Data Collection Data collection for the quantitative strand of the current study was obtained by distributing a pretest and posttest to all volunteer students of the control and treatment group. First all student volunteers of the control and treatment group took a pretest on the topic of osteology. Then all students were given a handout on osteology to complete over a 3-week study period. Students in the control and treatment group could use any study method they chose to study with during the 3-week study period while completing the handout. However, the students in the treatment group also had to use the treatment to study with. Students in the control group were offered the treatment after the investigation was complete. All students took the pretest before any learning took place and the posttest after the 3-week study period. Students took the pretest and posttest in the medical lab where they rotated around stations that were setup with examples of parts of the human skeletal system pertaining to the question that was being asked on the test. Students had 1 minute to answer the question before rotating to the next station. The rotation continued until all questions were answered. There were 50 fill-in-the blank type questions on the pretest and posttest. The pretest consisted of the same questions as the posttest, but were presented in a different order. The qualitative strand of the current study consisted of data collection through semi-structured interviews with student volunteers that had received the treatment during the quantitative strand of the current study. The interviews and observations of interviewees during the interview process took place using Skype® software. Four students from the treatment group volunteered to take part in the interview process. An electronic online survey using Survey Monkey® consisting of open-ended questions was also distributed to student volunteers that received the treatment during the quantitative strand of the current study. Thirteen students from the treatment group volunteered to take part in the electronic online survey. The interviews and surveys took place after the investigation had been completed and students had two weeks to participate in the Skype® interview process and complete the electronic online survey. Data for the qualitative strand of the current study was collected through the semi-structured interviews, the electronic online survey, and researcher’s notes and observations of student volunteers.

Data Analysis For the quantitative study the Statistical Package for the Social Sciences (SPSS®) software was used to run the ANCOVA test. Assumptions were met first by using the ANOVA test which controlled for the pre-test value while analyzing the dependent variable (post-test value) to see if

This article can be used for educational purposes only

a significant difference existed between the pre-test values for the treatment and control groups. It was important that the co-variant met the requirements to run the ANCOVA test before proceeding, because there should not be a statistically significant difference in the values between the control and treatment groups for the pre-test. The homogeneity and Levene’s assumptions tests for the data collected were met. The Levene's Test of Equality of Error Variances tests the null hypothesis that the error variance of the dependent variable is equal across groups. Seven volunteer students that took the pretest were unable to take the posttest. Therefore, two data sets were created. One data set consisted of the 63 students that completed both the pretest and the posttest, as the seven students that did not complete the posttest were deleted from the data set. The second data set included all 70 students’ scores and the missing scores for the seven students that were unable to take the posttest. For the second data set the seven missing posttest scores were imputed using SPSS®. Using the imputed values function SPSS® predicted what the seven missing posttest scores would be for the students that did not complete the posttest based on all pretest and posttest data collected. To check the validity of the results SPSS® was used to run the ANOVA and ANCOVA tests with both the deleted data set and the imputed values data set. The data for the qualitative strand of the current study collected through the semi-structured interviews, the electronic online survey, and researcher’s notes and observations of participants were uploaded to MAXQDA Plus® software. The data was sorted, categorized, and color coded. MAXQDA Plus® software was used to check for trends in data, cross references in data, and to analyze the qualitative data collected using graphs and charts (MAXQDA Plus Software, 2015). The survey results, interview results, and researcher’s notes and observations were analyzed resulting in triangulation of the qualitative data collected. Triangulation happens when three different types of data collection tools are used in a research study and the findings from all three data collection tools are compared to one another to check for the consistency of trends in the data collected (Merriam, 2009).

Quantitative Results The quantitative results of the current study found that the adjusted mean of the treatment group was slightly higher than the adjusted mean of the control group. However, there was no significant difference found between the two groups when testing for between-subjects effects for the post-test, while controlling for the pre-test value. Table 1 shows the results for the tests of between-subjects effects for the dependent variable “Pretest” using the deleted scores data set. Table 2 shows the results for the tests of between-subjects effects for the dependent variable “Pretest” using the imputed scores data set.

3

Table 1 shows the results for the tests of between-subjects effects for the dependent variable “Pretest” using the deleted scores data set.

Table 2 shows the results for the tests of between-subjects effects for the dependent variable “Pretest” using the imputed scores data set.

Table 1

Tests of Between-Subjects Effects for Deleted Scores Dependent Variable: Pre-test Type III Sum of Source Squares Df Mean Square Corrected 1.156a 1 1.156 Model Intercept 10873.172 1 10873.172 Group 1.156 1 1.156 Error 4794.757 61 78.603 Total 15704.500 63 Corrected Total 4795.913 62 a. R Squared = .000 (Adjusted R Squared = -.016) b. Computed using alpha = .05 (See APPENDIX G.)

F Sig. .015 .904

Partial Eta Squared .000

Noncent. Parameter .015

Observed Powerb .052

138.331 .000 .015 .904

.694 .000

138.331 .015

1.000 .052

Table 2 Tests of Between-Subjects Effects for Imputed Scores Dependent Variable: Pre-test Source Corrected Model Intercept

Type III Sum of Squares 6.914a 12676.629

1 1

Mean Square 6.914 12676.629

.089 162.262

Sig. .767 .000

6.914

1

6.914

.089

.767

Error

5312.457

68

78.124

Total

17996.000

70

5319.371

69

Group

Corrected Total a.

Df

F

R Squared = .001 (Adjusted R Squared = -.013) (See APPENDIX H.)

Table 1 and table 2 shows there was no statistically significant difference found between the adjusted mean for the control group and treatment group outcome scores for the pre-test for both the deleted scores data set and the

This article can be used for educational purposes only

imputed scores data set. The outcome for the significance level is highlighted in yellow. Table 1 shows p =.904 and Table 2 shows p =.767. Both tables show p >.05.

4

shows the results for the homogeneity test of betweensubjects effects for the dependent variable “posttest” using the imputed scores data set.

Table 3 shows the results for the homogeneity test of between-subjects effects for the dependent variable “posttest” using the deleted scores data set. Table 4

Table 3 Tests of Between-Subjects Effects for Deleted Scores Dependent Variable: Post-test - Homogeneity Type III Sum of Source Squares Df Mean Square F Sig. Corrected 7524.553a 3 2508.184 54.783 .000 Model Intercept 1867.542 1 1867.542 40.790 .000 Group 1.444 1 1.444 .032 .860 PreScore 6515.844 1 6515.844 142.317 .000 Group * 2.115 1 2.115 .046 .831 PreScore Error 2701.265 59 45.784 Total 54972.500 63 Corrected Total 10225.817 62 a. R Squared = .736 (Adjusted R Squared = .722) b. Computed using alpha = .05 (See APPENDIX G.)

Partial Eta Squared .736

Noncent. Parameter 164.348

Observed Powerb 1.000

.409 .001 .707 .001

40.790 .032 142.317 .046

1.000 .054 1.000 .055

Table 4 Tests of Between-Subjects Effects for Imputed Scores Dependent Variable: Post-test Homogeneity Type III Sum of Source Squares df Mean Square Corrected Model 8321.030a 3 2773.677 Intercept 2196.911 1 2196.911 Group

F 60.236 47.711

Sig. .000 .000

2.796

1

2.796

.061

.806

PreScore

7402.301

1

7402.301

160.757

.000

Group * PreScore Error

1.183 3039.068

1 66

1.183 46.046

.026

.873

Total

63704.717

70

Corrected Total

11360.098

69

a. R Squared = .732 (Adjusted R Squared = .720) (See APPENDIX H.)

Homogeneity tests for a significant difference between subjects. The relationship between the dependent variable (Y) and the covariate (X) must be linear for every independent variable. Homogeneity was tested using the ANOVA test. The variables where: covariate=Pre-test, dependent variable=Post-test, and fixed factor=Group using a custom model of Group*Pre-test. The Group*Pretest score results show a non-significant difference of

This article can be used for educational purposes only

p =.831 for the deleted scores data set shown in Table 3 and p =.873 for the imputed scores data set shown in Table 4, which is > .05. The outcome for the significance level is highlighted in yellow. Both data sets met the homogeneity assumption. No significant difference exists between the levels of the independent variable when controlling for the pre-test value.

5

the results for the tests of between-subjects effects for the dependent variable “Posttest” using the imputed scores data set.

The following test checked for a significant difference between the adjusted mean for the control and treatment group’s outcome scores for the posttest, while controlling for the pretest value. Table 5 shows the results for the tests of between-subjects effects for the dependent variable “Posttest” for the deleted scores data set. Table 6 shows

Table 5 Tests of Between-Subjects Effects for Deleted Scores Dependent Variable: Post-test Groups Type III Sum of Source Squares Df Mean Square Corrected 7522.438a 2 3761.219 Model Intercept 1993.387 1 1993.387 PreScore 7487.223 1 7487.223 Group 21.069 1 21.069 Error 2703.380 60 45.056 Total 54972.500 63 Corrected Total 10225.817 62 a. R Squared = .736 (Adjusted R Squared = .727) b. Computed using alpha = .05 (See APPENDIX G.)

F Sig. 83.478 .000

Partial Eta Squared .736

Noncent. Parameter 166.956

Observed Powerb 1.000

44.242 .000 166.175 .000 .468 .497

.424 .735 .008

44.242 166.175 .468

1.000 1.000 .103

Table 6 Tests of Between-Subjects Effects for Imputed Scores Dependent Variable: Post-test Groups Type III Sum Source of Squares Df Mean Square F a Corrected Model 8319.847 2 4159.923 91.675 Intercept 2310.919 1 2310.919 50.927 PreScore 8252.521 1 8252.521 181.866 Group 24.253 1 24.253 .534 Error 3040.251 67 45.377 Total 63704.717 70 Corrected Total 11360.098 69 a. R Squared = .732 (Adjusted R Squared = .724) (See APPENDIX H.)

Table 5 and table 6 show there was no statistically significant difference found between the adjusted mean for the control group and treatment group outcome scores for the posttest, while controlling for the pretest value, for both the deleted scores data set and the imputed scores

This article can be used for educational purposes only

Sig. .000 .000 .000 .467

Partial Eta Squared .732 .432 .731 .008

data set. The outcome for the significance level is highlighted in yellow. Table 5 shows p =.497 and Table 6 shows p =.467. The results from table 5 and table 6 show p >.05.

6

Table 7 compares the control and treatment group’s adjusted mean for the dependent variable “posttest” using the deleted scores data set. Table 8 compares the control

and treatment group’s adjusted mean for the dependent variable “posttest” using the imputed scores data set.

Table 7 Dependent Variable: Post-test Group Adjusted Mean for Deleted Scores Group Control

Mean 26.044a

Std. Error 1.226

95% Confidence Interval Lower Bound Upper Bound 23.593 28.496

Treatment 27.202a 1.169 24.865 29.540 a. Covariates appearing in the model are evaluated at the following values: Pre-test Score = 13.1587. (See APPENDIX G.)

Table 8 Dependent Variable: Post-test Group Adjusted Mean for Imputed Scores Group Control

Mean 26.757a

Std. Error 1.139

95% Confidence Interval Lower Bound Upper Bound 24.483 29.030

Treatment 27.935a 1.139 25.661 30.208 a. Covariates appearing in the model are evaluated at the following values: Pretest Score = 13.4571. (See APPENDIX H.)

Table 7 and table 8 show there was a slight increase in the adjusted mean for the treatment group outcome scores compared to the control group for both the deleted scores data set and the imputed scores data set. The outcome for the adjusted mean for the control and treatment group is highlighted in yellow. Using the deleted scores data set table 7 shows the adjusted mean for the control group is

Qualitative Results Since students in the treatment group could use other study methods besides the treatment to study with, the qualitative strand of the current study provided insightful results that helped explain the results concluded in the quantitative results analysis. During the qualitative analysis, several themes were discovered when analyzing the survey and interview results. The themes discovered were: Theme 1: Holograms as a learning tool; Theme 2: Effects on learning; Theme 3: Experiences with treatment; Theme 4: Ease of use; Theme 5: Treatment improvements; Theme 6: Future

This article can be used for educational purposes only

M =26.044a and the adjusted mean for the treatment group is M =27.202a. Using the imputed scores data set table 8 shows the adjusted mean for the control group is M =26.757a and the adjusted mean for the treatment group is M =27.935a. The adjusted mean for the control and the treatment groups are highlighted in yellow. use of holograms; Theme 7: Features; Theme 8: Use of Treatment; Theme 9: Study method most used; and Theme 10: Use of Technology. When cross-referencing the results of the online electronic surveys and the student interviews based on specific trends in data and counting how many times the occurrences of the trend appeared, the following results were concluded: The combined survey and interview data results indicated 82.9% of students perceived their experiences with the use of the augmented 3D holograms had a positive effect on their learning. Results further indicated 84.7% of students had positive meaningful experiences with the use of the augmented 3D holograms, and 87.5% of students held a positive perspective on the use of augmented 3D holograms as a

7

learning tool. Figure 4 shows an example of the coded analysis for the data collected through the online survey for

Student 8. Figure 5 shows the coded analysis for the data collected through the online interview for interviewee one.

Figure 4. Coded example of student survey for Student 8

This article can be used for educational purposes only

8

Figure 5. Coded example of Interview One

This article can be used for educational purposes only

9

One theme found in the data analysis was particularly insightful. Theme 5: Treatment Improvements, consisted of students’ ideas on how to improve the use of the augmented 3D holograms to provide maximum benefits to

learning and increase learning outcomes. Figure 6 shows student suggestions for treatment improvement and the number of times the suggestion was made.

Suggestions for Treatment Improvements

Figure 6. First Year Undergraduate Medical Students’ Suggestions for Treatment Improvements All students gave suggestions on features that could be added to the treatment and more advantageous ways to use the treatment to provide them with a learning tool that will increase their learning outcomes on a more profound level. Suggestions included fixing the glitches in the system, having better access to the treatment, adding interactive games and knowledge checks, adding self-study instructional modules, using the treatment during instructor-led lessons, using the treatment as a supplement, using the treatment as a review, making the treatment portable, adding the ability to view more than one object at a time to show connectivity, improve structure views, enhancing navigation, and adding more explanation and descriptions. Many students experienced glitches in the system when using the treatment and expressed that the treatment needed to be more accessible. The treatment was setup in the school library and students could only access it during library hours. The use of the treatment was also presented in an unstructured learning environment that would benefit from the use of self-study modules to supplement the use of the treatment. Even with these limitations the data collected indicated students found the use of the treatment to be beneficial to their learning.

Discussion The qualitative strand of the current study analyzed six previous similar studies, noted in the “Methods” section, that investigated the use of holograms as an educational learning tool. The analysis of these previous studies provided more insight to help examine the results for the quantitative strand of the current study. The results from previous studies were compared with the results obtained from the current study. Some limitations of the current study were apparent when the qualitative data for the

This article can be used for educational purposes only

current study was analyzed. These limitations caused the results of the quantitative strand of the current to be questioned. The limitations found through the qualitative data analysis include students’ experiences with glitches in the system when using the treatment and restricted access to the treatment. These limitations caused students not to use the treatment more often. Another limitation is the lack of a more controlled or structured learning environment in which the treatment was implemented. When this study was compared with other similar previous studies it was concluded that a more controlled and structured learning environment produced a higher significant increase in student learning outcomes with the use of holograms as a learning tool. The results of the qualitative strand of the current study produced further insight into the problem. Therefore, there is not enough evidence to prove the results of the quantitative strand of the current study are true. The current study’s quantitative results indicated that the outcome scores for students in the treatment group did show a slightly higher increase in the adjusted mean compared to the students in the control group, but there was no significant difference found between the two groups. However, qualitative results for the current study indicated that most students in the treatment group perceived their experiences with the use of the augmented 3D holograms were positive, the treatment had a positive and meaningful effect on their learning of the topic being studied, and they would use the treatment again. All students in the treatment group indicated there were glitches in the system when accessing the treatment and most students indicated that access to the treatment was too restricted. When comparing, and analyzing the results of the current study to previous similar studies the following conclusion was reached for the qualitative strand of the current study: A

10

higher significant difference in students’ learning outcome scores may be achieved with the use of the treatment if students do not experience problems when trying to use the treatment and have more access to the treatment in a more controlled and structured learning environment. This conclusion causes reasonable doubt when interpreting the results for the quantitative strand of the current study. If the quantitative results of the current study were analyzed without the qualitative results, it would be clear that the use of augmented 3D holograms for educational purposes does not significantly increase students’ learning outcome scores. However, it is important to consider the fact that even though the results for the quantitative strand of the current study did not show significance, there was a slightly higher increase in the adjusted mean of the treatment group’s learning outcome scores compared to the control group. When considering this fact and then analyzing the results from the qualitative strand of the current study that relate to the treatment’s accessibility; ease of use; students’ experiences with the treatment; and students’ perspectives on the treatments effect on their learning, reasonable doubt exists as to whether the results obtained from the quantitative strand of the current study are true. The implications of this study are that students seek new ways in which to learn and perceive the use of augmented 3D holograms as a new learning tool that has a positive effect on their learning. However, the new learning tool, augmented 3D holograms, should be used to its fullest

capabilities if a significant increase in student learning outcome scores are to be achieved. This can be done by adding more features to the technology for students to use when learning with the treatment, such as self-check quizzes, game features, a toggle button to view and hide labels and annotations, and the ability to view more than one object at a time to be able to visualize how the objects connect. Selecting the correct learning environments in which to use the new learning tool needs consideration, because the learning environment can influence the use of a new learning tool and how students’ learning is affected. Providing a more structured learning environment by using the treatment in an instructor-led classroom or adding selfstudy learning modules will help guide students through the learning process. The ease of use of the new learning tool and access to it are also important considerations. Students learning scores may increase more if students are able to use the learning tool more, but if the learning tool is hard to use or hard to access then this will deter students from wanting to use it. New types of learning environments need to be created for the students of today, because advances in technology have produced a new type of learner. The students of today need to be challenged and seek to learn through methods that challenge them and keep them engaged. Augmented 3D holograms have the capability to transform the students learning environment, challenge students, keep them engaged, and increase their learning outcomes.

Acknowledgments I would like to acknowledge my chair, Dr. Kelly Gatewood, and committee members Dr. Kelly Schwirzke and Dr. Susan Koba, Keiser University, Dr. Scott Lozanoff, and all students that participated in this study for their time and efforts in helping to complete this study. Correspondence to: Sharon Golden, PhD; Email: [email protected] or [email protected]

This article can be used for educational purposes only

11

References 1.

2.

3.

4.

5.

6.

7.

8.

9.

Ahmad, S. A., Abdullahi, I. M., & Usman, M. (2015). General attitude and acceptance of holography in teaching among lecturers in Nigerian colleges of education. The IAFOR Journal of Education, spec ed, Summer, 140-160. Retrieved from http://iafor.org/archives/journals/education/journalof-education-specialeditioncontents/Ahmad_Abdullahi_Usman.pdf Autodesk, Inc. (2016). Maya. Make it with Maya. Retrieved from http://www.autodesk.com/products/maya/overview Chmiel, K. (2014). The constructivists approach to teaching tomorrow’s general music professor. Illinois Music Education Conference. Retrieved from http://www.ilmea.org/site_media/filer_public/2014/0 1/10/chmiel_-_constructivist.pdf Creswell, J. W. (2013a). Developing mixed methods research with Dr. John W. Creswell. Retrieved from https://youtu.be/PSVsD9fAx38 Creswell, J. W. (2013b). Telling a Complete Story with Qualitative and Mixed Methods Research–Dr. John W. Creswell. Retrieved from https://www.youtube.com/watch?v=l5e7kVzMIfs Ghuloum, H. (2010). 3D hologram technology in learning environment. Proceedings of Informing Science & IT Education Conference (pp. 694–704).. Retrieved from http://www.google.com/url?sa=t&rct=j&q=&esrc=s &source=web&cd=1&ved=0 CCkQFjAA&url=http%3A%2F%2Fproceedings.info rmingscience.org%2FInSIT E2010%2FInSITE10p693704Ghuloum751.pdf&ei=4eNYVbLIMYyZsAXDt4 GoBg&usg=AFQjCNHMvlv T9xcsKU_6aX8DQyE4iBr1RA&bvm=bv.93564037, d.b2w Hackett, M. (2013). Higher education medical holography for basic anatomy training. Interservice/Industry Training, Simulation, and Education Conference (I/ITSEC; pp. 1–10). Retrieved from http://cdn2.hubspot.net/hub/151303/file-476620026pdf/docs/higher education medical_holograms_whitepaper.pdf Hite, R., & Childers, G. (2016). Teachers’ pedagogical perceptions of novel 3-D haptic- enabled virtual reality technology. Retrieved from http://www.fi.ncsu.edu/news/new-research-revealsexperienced-teachers-aremore-open-to-using-newtechnology/ HP Development Company, L. P. (2016). HP Zvr 23.6-inch virtual reality display. Overview. Retrieved from http://www8.hp.com/us/en/products/monitors/product - detail.html?oid=7445887#!tab=features

This article can be used for educational purposes only

10. Johnson, L., Adams Becker, S., Estrada, V., Freeman, A. (2015). NMC Horizon Report: 2015 Higher Education Edition. Austin, Texas: The New Media Consortium. 11. MAXQDAplus. (2015). MAXQDA Plus. The art of data analysis. Retrieved from http://www.maxqda.com/products/maxqdaplus 12. Merriam, S., B. (2009). Qualitative Research. A Guide to Design and Implementation. San Francisco, CA: Jossey-Bass. 13. Rizov, T., & Rizova, E. (2015). Augmented reality as a teaching tool in higher education. International Journal of Cognitive Research in Science, Engineering and Education 3(1). Retrieved from http://DialnetAugmentedRealityAsATeachingToolInHigherEducat ion-5109026.pdf 14. Salvetti, F., & Bertagni, B. (2014). e-REAL: Enhanced reality lab. International Journal of Advanced Corporate Learning, 7(3), 41. doi:10.3991/ijac.v7i3.4033 15. Tallitsch, R., Beck, A., Kelley, K., & Peters, B. (2012). The effects of computer-assisted instruction in teaching human anatomy: An experimental study. Retrieved from http://www.augustana.edu/users/bitallitsch/nsf_grant. html 16. Unity Technologies. (2016). The best development platform for creating games. Retrieved from https://unity3d.com 17. zSpace, Inc. (2016). Medical learning solutions. Retrieved from http://zspace.com/medical-learning

12