This zip file contains data files for 3 activities described in the accompanying PPT slides 1. an excel spreadsheet for analysing gain scores in a 2 group, 2 times data array. this activity requires access to –https://campbellcollaboration.org/research-resources/effect-size-calculator.html to calculate effect size.2. an AMOS path model and SPSS data set for an autoregressive, bivariate path model with cross-lagging. This activity is related to the following article: Brown, G. T. L., & Marshall, J. C. (2012). The impact of training students how to write introductions for academic essays: An exploratory, longitudinal study. Assessment & Evaluation in Higher Education, 37(6), 653-670. doi:10.1080/02602938.2011.5632773. an AMOS latent curve model and SPSS data set for a 3-time latent factor model with an interaction mixed model that uses GPA as a predictor of the LCM start and slope or change factors. This activity makes use of data reported previously and a published data analysis case: Peterson, E. R., Brown, G. T. L., & Jun, M. C. (2015). Achievement emotions in higher education: A diary study exploring emotions across an assessment event. Contemporary Educational Psychology, 42, 82-96. doi:10.1016/j.cedpsych.2015.05.002andBrown, G. T. L., & Peterson, E. R. (2018). Evaluating repeated diary study responses: Latent curve modeling. In SAGE Research Methods Cases Part 2. Retrieved from http://methods.sagepub.com/case/evaluating-repeated-diary-study-responses-latent-curve-modeling doi:10.4135/9781526431592

The data have been collected from survey questionnaires based on a 5 point Likert scale and inserted into the SPSS software for screening. Later, these data have been analyzed through PLS software. The data are consisted of drivers of Institutional pressure such as Coercive pressure, Mimetic pressure and Normative pressure. The data also are consisted of the variables such as Product Stewardship and the Adoption Propensity of Green ICT (Information Communication Technology) in Malaysia. Result shows that out of three (3) drivers, only Normative Pressure is significant towards the Adoption Propensity of Green ICT in Malaysia. Product stewardship is also significant towards the Adoption Propensity of Green ICT in Malaysia. Thus, it gives the policy makers a distinct direction regarding the right factors to consider in order to have Green ICT industry in Malaysia.

This dataset consists of three data folders for the eye-tracking experiment conducted within the NESP 3.2.3 project (Tropical Water Quality Hub): Folder (1) The folder of Eye-tracking videos contains 66 Tobii recordings of participants’ eye movements on screen, Folder (2) The Heatmaps folder includes 21 heatmaps created by Tobii eye-tracking software on the basis of 66 participants’ data and Folder (3) The input folder has 21 original pictures used in eye-tracking experiment. Moreover, The dataset also includes 1 excel file representing eye-tracking data extracted from Tobii software and participant interview results, 1 SPV. file as the input of SPSS data analysis process and 1 SPV. file as the output of data analysis process.

Methods: This dataset resulted from both input and output data of eye-tracking experiments. The input includes 21 underwater pictures of the Great Barrier Reef, selected from online searching with the keyword “Great Barrier Reef”. These pictures are imported to Tobii eye-tracking software to design the eye-tracking experiments. 66 participants were recruited using convenience sampling in this study. They were asked to sit in front of a screen-based eye-tracking equipment (i.e. Tobii T60 eye-tracker) after providing informed consent. Participants were free to look at each picture on screen as long as they wanted during which their eye movements were recorded. They also rated each picture on a 10-point beauty scale (1-Not beautiful at all, 10-Very beautiful) and a 10-point expectation scale (1-Not at all, 10-Very much). After the experiment, 40 subjects were also interviewed to identify the areas of interest (AOI) in each picture and to rate the beauty of these AOIs. Eye-tracking data was then extracted from Tobii eye-tracking device including participants’ eye-tracking recordings, heatmaps (i.e. images showing viewers’ attention focus) and raw eye-tracking measures (i.e. picture beauty, time to first fixation, fixation count, fixation duration and total visit time) using XLSX. download format. Raw eye-tracking data was then imported to IBM SPSS using SAV. format for data analysis which results in a SPV. output file.

Further information can be found in the following publication: Scott, N., Le, D, Becken, S., and Connelly, R. (2018 Submitted) Measuring perceived beauty of the Great Barrier Reef using eye tracking. Journal of Sustainable Tourism. Becken, S., Connolly R., Stantic B., Scott N., Mandal R., Le D., (2018), Monitoring aesthetic value of the Great Barrier Reef by using innovative technologies and artificial intelligence, Griffith Institute for Tourism Research Report No 15.

Format: The project dataset includes 132 eye-tracking videos of AVI. format, 21 heatmaps of PNG. format, 21 pictures of JPEG. format, 1 XLSX. format document representing raw eye-tracking measures and interview data, 1 SAV. format document as the input of data analysis and 1 SPV. format file showing data analysis results.

Data Dictionary:

Q1, Q2, Q3, Q4, Q5, Q6, Q7, Q8, Q9, Q10: Names of pictures used in the eye-tracking experiment 2. 3Q1, 3Q2, 3Q3, 3Q4, 3Q5, 3Q6, 3Q7, 3Q8, 3Q9, 3Q10, 3Q11: Names of pictures used in the eye-tracking experiment 3.

Raw Eye tracking Measurements excel spreadsheets:

Tab - Picture: INDEX: the 10-point scale showed to participants VALUE: meaning of the 10-point scale Q1.1: Beauty score Q1.2: Expectation score

Tab - Area of Interest (AOI)" TIME TO FIRST FIXATION_Q1: Time to first fixation in the picture Q1 (i.e. i.e. the average time from the beginning of the recording until the respective picture was first fixated upon) TOTAL FIXATION DURATION_Q1: Fixation duration in the picture Q1 (i.e. the average length of all fixations during all recordings in the whole picture). A longer fixation means that the object is more engaging in some way. FIXATION COUNT_Q1: Fixation count in the picture Q1 (i.e. the average number of fixations in the picture). TOTAL VISIT DURATION_Q1: Total time visit for the picture Q1 (i.e. the average time participants spent looking at a picture). TIME TO FIRST FIXATION_AOI1: Time to first fixation in the AOI identified in the picture Q1 (i.e. i.e. the average time from the beginning of the recording until the respective picture was first fixated upon) TOTAL FIXATION DURATION_AOI1: Fixation duration in the AOI identified in the picture Q1 (i.e. the average length of all fixations during all recordings in the whole picture). A longer fixation means that the object is more engaging in some way. FIXATION COUNT_AOI1: Fixation count in the AOI identified in the picture Q1 (i.e. the average number of fixations in the picture). TOTAL VISIT DURATION_AOI1: Total time visit for the AOI identified in the picture Q1 (i.e. the average time participants spent looking at a picture).

Tab - AOI interview: AOI IDENTIFIED: The AOI that is the most mentioned by participants NUMBER OF PARTICIPANTS: the number of participants who mentioned the AOI in the previous column. BEAUTY MEAN: The average beauty score of the correspondent AOI rated by 40 participants. AOI-1: The AOI identified by the correspondent participant. RATING: the beauty score associated to the AOI identified by the correspondent participant.

Tab - Analysis: REC: Recording PICTURE: Picture number BEAUTY: The average beauty score of the correspondent picture by 66 participants EXPECTATION: The average expectation score of the correspondent picture by 66 participants AOI BEAUTY: The average beauty score of the AOI identified in the correspondent picture by interviewed participants. PICTURE 1st TIME: The average time to first fixation in the correspondent picture (i.e. i.e. the average time from the beginning of the recording until the respective picture was first fixated upon) by 66 participants PFDURATION: The average fixation duration in the correspondent picture (i.e. the average length of all fixations during all recordings in the whole picture) by 66 participants PFCOUNT: The average fixation count in the correspondent picture (i.e. the average number of fixations in the picture) by 66 participants PTING VISIT: The average of total time visit for the correspondent picture (i.e. the average time participants spent looking at a picture) by 66 participants AOI 1stTIME: The average time to first fixation in the AOI identified in the correspondent picture (i.e. i.e. the average time from the beginning of the recording until the respective picture was first fixated upon) by 66 participants AOIFDURATION: The average fixation duration in the AOI identified in the correspondent picture (i.e. the average length of all fixations during all recordings in the whole picture) by 66 participants AOIFCOUNT: The average fixation count in the the AOI identified in correspondent picture (i.e. the average number of fixations in the picture) by 66 participants AOITIMEVISIT: The average of total time visit for the AOI identified in the correspondent picture (i.e. the average time participants spent looking at a picture) by 66 participants

References:

Scott, N., Le, D, Becken, S., and Connelly, R. (2018 Submitted) Measuring perceived beauty of the Great Barrier Reef using eye tracking. Journal of Sustainable Tourism.

Becken, S., Connolly R., Stantic B., Scott N., Mandal R., Le D., (2018), Monitoring aesthetic value of the Great Barrier Reef by using innovative technologies and artificial intelligence, Griffith Institute for Tourism Research Report No 15.

Data Location:

This dataset is filed in the eAtlas enduring data repository at: data esp3\3.2.3_Aesthetic-value-GBR

Open Science in (Higher) Education – data of the February 2017 survey

This data set contains:

• Full raw (anonymised) data set (completed responses) of Open Science in (Higher) Education February 2017 survey. Data are in xlsx and sav format.
• Survey questionnaires with variables and settings (German original and English translation) in pdf. The English questionnaire was not used in the February 2017 survey, but only serves as translation.
• Readme file (txt)

Survey structure

The survey includes 24 questions and its structure can be separated in five major themes: material used in courses (5), OER awareness, usage and development (6), collaborative tools used in courses (2), assessment and participation options (5), demographics (4). The last two questions include an open text questions about general issues on the topics and singular open education experiences, and a request on forwarding the respondent’s e-mail address for further questionings. The online survey was created with Limesurvey[1]. Several questions include filters, i.e. these questions were only shown if a participants did choose a specific answer beforehand ([n/a] in Excel file, [.] In SPSS).

Demographic questions

Demographic questions asked about the current position, the discipline, birth year and gender. The classification of research disciplines was adapted to general disciplines at German higher education institutions. As we wanted to have a broad classification, we summarised several disciplines and came up with the following list, including the option “other” for respondents who do not feel confident with the proposed classification:

• Natural Sciences
• Arts and Humanities or Social Sciences
• Economics
• Law
• Medicine
• Computer Sciences, Engineering, Technics
• Other

The current job position classification was also chosen according to common positions in Germany, including positions with a teaching responsibility at higher education institutions. Here, we also included the option “other” for respondents who do not feel confident with the proposed classification:

• Professor
• Special education teacher
• Academic/scientific assistant or research fellow (research and teaching)
• Academic staff (teaching)
• Student assistant
• Other

We chose to have a free text (numerical) for asking about a respondent’s year of birth because we did not want to pre-classify respondents’ age intervals. It leaves us options to have different analysis on answers and possible correlations to the respondents’ age. Asking about the country was left out as the survey was designed for academics in Germany.

Remark on OER question

Data from earlier surveys revealed that academics suffer confusion about the proper definition of OER^[2]. Some seem to understand OER as free resources, or only refer to open source software (Allen & Seaman, 2016, p. 11). Allen and Seaman (2016) decided to give a broad explanation of OER, avoiding details to not tempt the participant to claim “aware”. Thus, there is a danger of having a bias when giving an explanation. We decided not to give an explanation, but keep this question simple. We assume that either someone knows about OER or not. If they had not heard of the term before, they do not probably use OER (at least not consciously) or create them.

Data collection

The target group of the survey was academics at German institutions of higher education, mainly universities and universities of applied sciences. To reach them we sent the survey to diverse institutional-intern and extern mailing lists and via personal contacts. Included lists were discipline-based lists, lists deriving from higher education and higher education didactic communities as well as lists from open science and OER communities. Additionally, personal e-mails were sent to presidents and contact persons from those communities, and Twitter was used to spread the survey.

The survey was online from Feb 6^th to March 3^rd 2017, e-mails were mainly sent at the beginning and around mid-term.

Data clearance

We got 360 responses, whereof Limesurvey counted 208 completes and 152 incompletes. Two responses were marked as incomplete, but after checking them turned out to be complete, and we added them to the complete responses dataset. Thus, this data set includes 210 complete responses. From those 150 incomplete responses, 58 respondents did not answer 1st question, 40 respondents discontinued after 1st question. Data shows a constant decline in response answers, we did not detect any striking survey question with a high dropout rate. We deleted incomplete responses and they are not in this data set.

Due to data privacy reasons, we deleted seven variables automatically assigned by Limesurvey: submitdate, lastpage, startlanguage, startdate, datestamp, ipaddr, refurl. We also deleted answers to question N^o 24 (email address).

References

Allen, E., & Seaman, J. (2016). Opening the Textbook: Educational Resources in U.S. Higher Education, 2015-16.

First results of the survey are presented in the poster:

Heck, Tamara, Blümel, Ina, Heller, Lambert, Mazarakis, Athanasios, Peters, Isabella, Scherp, Ansgar, & Weisel, Luzian. (2017). Survey: Open Science in Higher Education. Zenodo. http://doi.org/10.5281/zenodo.400561

Contact:

Open Science in (Higher) Education working group, see http://www.leibniz-science20.de/forschung/projekte/laufende-projekte/open-science-in-higher-education/.

[1] https://www.limesurvey.org

[2] The survey question about the awareness of OER gave a broad explanation, avoiding details to not tempt the participant to claim “aware”.