Introduction and literature
Perceived value and use of analytics in higher education
Challenges of harnessing analytics in higher education
Methods and procedures
Analysis
Participants demographic
Demography item | N (%) |
---|---|
Role | |
Vice Chancellor | 0 (0) |
Deputy Vice Chancellor | 1 (1) |
Pro-Vice Chancellor | 3 (3) |
Dean | 1 (1) |
Head of Department | 20 (23) |
Director | 9 (10) |
Manager | 20 (23) |
Coordinator | 13 (15) |
Other | 21 (24) |
Length of time in the role | |
Less than a year | 9 (10) |
1–5 | 48 (54) |
5–10 | 24 (27) |
10–15 | 7 (8) |
15–20 | 0 (0) |
More than 20 | 1 (1) |
Ethnicity | |
European | 71 (82) |
Māori | 0 (0) |
Asian | 5 (6) |
Pacific | 0 (0) |
Middle Eastern | 2 (2) |
Latin American | 1 (1) |
African | 1 (1) |
Indigenous | 0 (0) |
Other | 5 (6) |
Education | |
Bachelor | 10 (11) |
Master | 25 (28) |
PhD | 44 (50) |
Other | 9 (10) |
Findings
Perceptions of analytics: Textual analysis
Word | Length | Count | Weighted (%) | Similar Words |
---|---|---|---|---|
data | 4 | 58 | 13.2 | data, inform, information, informative |
analysis | 8 | 15 | 3.4 | analysis |
learning | 8 | 15 | 2.7 | know, knowledge, learning, reading, see, take, teaching |
student | 7 | 12 | 2.7 | student, students |
numbers | 7 | 12 | 2.7 | come, comes, numbers |
using | 5 | 11 | 2.4 | purposes, role, usage, use, using, utilised |
statistics | 10 | 10 | 2.3 | statistic, statistical, statistics |
course | 6 | 9 | 1.8 | course, courses, forming, line, tracking, trends |
research | 8 | 8 | 1.8 | research |
decision | 8 | 8 | 1.8 | conclusions, decision, decisions |
design | 6 | 7 | 1.3 | design, designers, destinations’, indicators, intent, invented, purposes |
analyzing | 9 | 7 | 1.6 | analyze, analyzing |
measurement | 11 | 7 | 1.5 | bar, evaluation, measurement, measures, metrics |
understanding | 13 | 7 | 1.1 | discern, reading, see, translation, understand, understanding |
big | 3 | 6 | 1.4 | big, large |
word | 4 | 5 | 1.1 | word, wrangling |
graphs | 6 | 5 | 1.1 | charts, graphs |
collected | 9 | 5 | 1.1 | collected, collecting, collection |
achievement | 11 | 5 | 1.1 | achievement, management, succeed, success |
prediction | 10 | 4 | 0.9 | prediction |
Google | 6 | 4 | 0.9 | Google |
complete | 8 | 4 | 0.9 | complete, completing, completion |
extraction | 10 | 4 | 0.8 | education, extraction |
describe | 8 | 4 | 0.8 | describe, key, line, reporting |
systems | 7 | 3 | 0.7 | systemized, systems |
support | 7 | 3 | 0.7 | help, support |
processing | 10 | 3 | 0.7 | actionable, processing |
mining | 6 | 3 | 0.7 | mining |
better | 6 | 3 | 0.7 | best, better |
based | 5 | 3 | 0.7 | based, basis |
existing | 8 | 3 | 0.6 | existing, university, world |
evidence | 8 | 3 | 0.5 | discern, evidence |
tool | 4 | 2 | 0.5 | tool, tools |
strategic | 9 | 2 | 0.5 | strategic |
retention | 9 | 2 | 0.5 | retention |
quantitative | 12 | 2 | 0.5 | quantitative |
performance | 11 | 2 | 0.5 | performance |
monitor | 7 | 2 | 0.5 | monitor, monitoring |
environment | 11 | 2 | 0.5 | environment, environments |
enrolment | 9 | 2 | 0.5 | enrolment, recruitment |
buzz | 4 | 2 | 0.5 | buzz |
blank | 5 | 2 | 0.5 | blank, space |
applications | 12 | 2 | 0.5 | applications |
publications | 12 | 2 | 0.3 | publications, world |
engaged | 7 | 2 | 0.3 | engaged, take |
trendy | 6 | 1 | 0.2 | trendy |
KPIs | 4 | 1 | 0.2 | KPIs |
intervention | 12 | 1 | 0.2 | intervention |
interaction | 11 | 1 | 0.2 | interaction |
insights | 8 | 1 | 0.2 | insights |
higher | 6 | 1 | 0.2 | higher |
hidden | 6 | 1 | 0.2 | hidden |
generated | 9 | 1 | 0.2 | generated |
enhance | 7 | 1 | 0.2 | enhance |
EFTS | 4 | 1 | 0.2 | EFTS |
DSS | 3 | 1 | 0.2 | DSS |
driven | 6 | 1 | 0.2 | driven |
discovery | 9 | 1 | 0.2 | discovery |
decision-making | 14 | 1 | 0.2 | decision-making |
Structural
“Information or data relating to higher education. I think it perhaps could be information similar to market data.”
“Data collected for a range of purposes.”
Functional
“Analysis of metrics such as research outputs (publications, etc.), student completions and external revenue.”
“Data analysis of students such as achievement rates, both regarding course and qualification completion rates.”
“Use of data to answer difficult and continuing questions - as a discipline to teach and as a research tool, unique for student achievement.”
“Systemized analysis or sorting of data with the intent of producing meaningful information.”
Structural-functional
“Analysis of the statistical data collected on students in different courses, for example: how many students attend a course, how many completed the program, demographics, etc.”
“Big Data is using already existing data to make judgments.”
“Statistics. Data that allows me to make decisions based on the information.”
The value of analytics in higher education
Student support
“Better tracking of student performance over time will allow for identification of both students who excel and could be targeted/nurtured for further education (postgraduate), as well as early detection and intervention for students who may be struggling before they fail their course.”
Resource optimisation
“Especially resources use in an institution can be well-optimized using particular sort of analysis.”
Faculty performance
“Improvement of faculty and research performance by extracting the right data and getting insightful ('meaningful') data.”
Administrative services
Possible institutional benefits of using analytics | N (%) |
---|---|
Understanding student demographics and behaviors | 53 (61) |
Improving faculty performance | 52 (60) |
Optimizing use of resources | 51 (59) |
Improving administrative services | 44 (51) |
Creating data transparency/sharing/federation | 41 (47) |
Helping students learn more efficiently/graduate | 40 (46) |
Reducing administrative costs | 32 (37) |
Recruiting students | 28 (32) |
Containing/lowering costs of education | 27 (32) |
Other | 11 (13) |
Demonstrating higher education’s effectiveness/efficiency | 0 |
“From my perspective, the greatest benefit is information on what is happening in the wider market and how can we use this to increase sales/enrollments.”
“Universities are large businesses with high levels of accountability, and they need the best information and evidence available to support their decision-making.”
“Good decisions are based on good data.”
Current institutional use of analytics | N (%) |
---|---|
Student enrollment management | 62 (70) |
Student progress | 58 (65) |
Finance and budgeting | 58 (65) |
Library | 43 (48) |
Central IT | 41 (46) |
Faculty teaching performance | 41 (46) |
Student learning | 39 (44) |
Faculty research performance | 38 (43) |
Human resources | 35 (39) |
Progress of strategic plan | 34 (38) |
Research administration | 33 (37) |
Instructional management | 23 (26) |
Procurement | 22 (25) |
Facilities | 19 (21) |
Cost to complete degree | 17 (19) |
Alumni/advancement | 15 (17) |
Faculty promotion and tenure | 14 (16) |
Other | 6 (7) |