Automating KPI Measurement: A Sustainable Solution for Educational Accreditation

Abstract

This paper examines the use of interactive Google Sheets for the automatic measurement of key performance indicators (KPIs) in higher education, particularly in the context of academic accreditation. As institutions face increasing pressure to demonstrate quality and effectiveness, reliable data tracking and reporting have become essential. Traditional methods of managing academic records and performance metrics can be cumbersome and error-prone, underscoring the need for an automated solution. By leveraging Google Sheets’ dynamic capabilities, institutions can efficiently monitor KPIs related to student performance, curriculum effectiveness, and faculty qualifications. This approach allows for real-time data updates, facilitating timely insights that support accreditation processes and continuous improvement initiatives. The study outlines the design and implementation of a dynamic Google Sheets framework tailored to higher education KPIs, highlighting its benefits in enhancing data accuracy, reducing administrative burden, and fostering collaboration among faculty and administration. Through illustrative case studies, we demonstrate how this tool empowers institutions to meet accreditation standards and drive educational excellence. Ultimately, the research emphasizes the potential of automation to transform academic record management and enhance institutional accountability in higher education.

1. Introduction

In recent years, academic accreditation has increasingly emphasized data-driven accountability, making key performance indicators (KPIs) crucial for demonstrating institutional effectiveness and educational quality [1,2,3]. KPIs provide objective measures across various domains—ranging from student outcomes to faculty qualifications—ensuring that institutions or degree programs meet specific standards set by accrediting bodies [4,5,6]. Despite their high significance, managing and monitoring KPIs remains a challenge, often due to resource constraints and the labor-intensive nature of data collection and reporting [7,8]. As a result, there is a critical need for automated systems that can streamline KPI tracking, improve data accuracy, and reduce administrative burden.

Advancements in automation technologies have shown promise in addressing these challenges by minimizing manual input and errors, thus enhancing the reliability of data used for institutional or program-level assessments [9,10]. Various cloud-based tools are particularly suited for academic environments due to their flexibility, accessibility, and real-time collaboration capabilities [11,12]. Google Sheets, for instance, has emerged as an effective solution for dynamic data management in education, with functionalities like Google Apps Script and Google Data Studio that enable live data updates, automated calculations, and customized reporting [13,14]. Its accessibility and adaptability make Google Sheets an ideal tool for institutions seeking to enhance their KPI measurement frameworks, aligning with accreditation requirements while fostering a culture of continuous improvement [15].

Furthermore, using Google Sheets (https://workspace.google.com/products/sheets/) (accessed on 20 November 2024) for KPI automation provides several benefits, such as enhanced collaboration among departments, transparent data sharing, and simplified access to current information for effective decision-making [6,16]. These features not only lessen workload but also empower academic staff by making data more accessible, allowing them to participate in the continuous assessment process more effectively [7,8]. Automating educational management enhances data handling and supports the accreditation process by providing timely and accurate metrics, essential for both internal and external audits [16,17].

This paper explores the use of dynamic Google Sheets as an automated tool to track and report KPIs in higher education, particularly in the context of academic accreditation. The free nature of Google Sheets, coupled with its dynamic capabilities, presents a compelling case for its adoption by institutions seeking to streamline their data management processes. Unlike expensive, proprietary software systems, Google Sheets can be implemented without an additional financial burden, making it an especially attractive option for budget-conscious institutions.

In this paper, we discuss the specific ways in which Google Sheets can be used to improve KPI management in higher education. Through a series of illustrative case studies, we demonstrate how this tool can be adapted to meet the unique needs of different institutions, regardless of size or focus. Whether used to track student achievement, monitor faculty productivity, or assess the effectiveness of academic programs, Google Sheets offers a dynamic, scalable solution that supports both accreditation and ongoing improvement efforts.

2. Planning and Creation of Google Sheets

In this section, we will discuss the creation of Google Sheets based on requirements at different levels of academic accreditation. We will start with individual faculty members and end with institutional records.

Before processing for the formation of a Google Sheet, it is required to understand a few basic terms used regularly in academic accreditation and quality assurance activities.

• Academic accreditation: Academic accreditation is a process where educational institutions are evaluated based on predefined standards. It ensures the quality and effectiveness of the institution’s educational programs.
• Key performance indicators (KPIs): These are quantifiable metrics that help assess the performance of the institution or specific programs, ensuring compliance with accreditation standards. There are several significant benefits of measuring KPIs; some examples are as follows:

  –

  Continuous improvement of academic quality.

  –

  Alignment with national or international standards.

  –

  Ensuring accountability and transparency in educational outcomes.

  –

  Enhancing the program’s and institution’s reputation and student success.

• Identifying Key Performance Indicators for Academic Accreditation: Before creating a Google Sheet, it is crucial to identify the relevant KPIs that align with accreditation requirements. Depending on the accreditation body in the national or international framework, any programs or institutions have a set of predefined general key performance indicators. Any degree program or institution aiming for academic accreditation should comply with the minimum number of KPIs prescribed by the accreditation body. However, the program needs to introduce a further set of their own in association with the nature of the program and distinguish practices aligned with the requirements of the program. In a similar pattern, each institute has to define several sets of KPIs depending on several different parameters. For the purpose of the reader’s full understanding, we have listed a few common KPIs below:

  –

  Student Learning Outcomes: metrics to assess students’ mastery of course objectives.

  –

  Graduation Rates: the percentage of students who complete their programs on time.

  –

  Retention Rates: the percentage of students who return after their first year.

  –

  Faculty Qualifications: the number of faculty members with terminal degrees or relevant qualifications.

  –

  Student Satisfaction: results from surveys assessing students’ experience and satisfaction.

  –

  Employment Rates: percentage of graduates who secure employment in their field of study.

  –

  Professional development plan and activities

  *

  Research publications;

  *

  Presentations: scientific seminars and conferences;

  *

  Funded research projects;

  *

  Training programs: attended, organized, or trainer;

  *

  Post-graduate thesis guidance;

  *

  Undergraduate research project guidance;

  *

  Citation of research;

  *

  Academic guidance;

  *

  Rewards and patents.

In this study, we establish a Google Sheet to demonstrate the key performance indicators related to the professional development activities for faculty members. From the record of academic guidance, we can also easily calculate the graduation and retention rate.

Since the aim here is to collect information about an individual faculty member’s professional development in each year, our first priority is to set up the interactive sheet for each individual faculty. In this study, we consider a sample of 50 faculties in total, of which 30 are male and 20 are female. All these experimental data are samples and not associated with any organizational data.

2.1. Creation of Individual Faculty Members’ Portfolios

A proper pre-planning is required before the creation of an individual faculty member’s portfolio. Since we are presenting the ideas to measure KPIs related to the faculty’s professional development activities, this portfolio sheet will have a total of thirteen tabs, as shown in Figure 1. Next, we are going to present the content in each tab with an explanation of its significance.

2.1.1. Cover Page

A cover page can be added as a first tab with the following minimum information

Table 1. An example of cover page on faculty portfolio page.

Name	write your full name here
Current Academic Rank	write your current rank
Scopus profile	past Scopus profile link
Google Scholar profile	past Google Scholar profile link
ORCID	past ORCID link

:

To facilitate easy filling, some drop-downs can be added in the academic rank part, as shown in Figure 2. A drop-down menu is always helpful to keep information uniformly filled and further assists in filtering information. As per the requirements, further information can be requested on the cover page.

2.1.2. Research Publications

A well-planned header in the Google Sheet by the name of Research publications is required to achieve better analysis of the KPI data. We present a sample of such a header in

Table 2. Header for Research Publications tab.

Serial No.

year

Month

No. department authors

No. university authors

No. authors other university

Article details

DOI

Journal Rank

Funded by

Aligned with Univ. Identity

Collaborated university

.

Next, we are going to explain the significance of each heading’s contents.

• Published year and months: Inclusion of both the publication year and month as separate columns in data collection sheets allows flexibility for both annual and academic year analyses. Data can be easily grouped or filtered by either, depending on the requirement.
• Number of authors: Classifying author data into three categories—departmental, university, and outside university—provides valuable insights into the level of research collaboration across different scopes. Here, we describe how this classification can be leveraged effectively:

  –

  Departmental Collaboration: These data are helpful to identify intra-departmental collaboration patterns. High numbers of departmental authors may indicate strong team-based research within the department but limited exposure beyond that scope. This is useful for evaluating internal capacity-building and the strength of specialized areas.

  –

  University Collaboration (Inter-Departmental or Colleges): This set of data provides insight on interdisciplinary collaboration within the university. High levels of inter-departmental co-authorship suggest that the university promotes cross-functional research, potentially leading to innovative solutions in emerging fields.

  –

  Outside University Collaboration (National and International): These data offer insights into the reach and influence of the institution’s research network at the national and global levels. Collaboration with external institutions reflects the visibility and influence of the university’s research and contributes to raising the institution’s reputation internationally.

• Article details: Complete details about the published article are required here. The possible information that are useful are as follows:

  –

  Author(s) name;

  –

  Publication title;

  –

  Journal’s abbreviated name;

  –

  Volume, numbers, year, and page number.

• DOI: Inclusion of the Digital Object Identifier (DOI) ensures direct access to the article’s webpage for verification purposes, facilitating easy retrieval of accurate information. Since the data collection mechanism accounts for individual faculty members, collaborative publications may appear multiple times across different faculty profiles. By leveraging the DOI, duplication in publication counts can be avoided, as the unique DOI allows for efficient filtering of repeated entries, ensuring an accurate tally of unique research outputs.
• Journal Rank: Journal rank is a key element in KPI measurement as it reflects research quality, influences institutional reputation, supports faculty evaluations, and plays a role in meeting accreditation standards. Incorporating it into automated systems like dynamic Google Sheets can help streamline tracking and reporting for accreditation purposes. In addition to the journal rank, journal impact factors can also be added in a separate column if required.
• Funding associated with the publications: Publications linked to funded research often have a higher chance of being published in high-impact journals. These publications not only elevate the reputation of individual researchers but also the institution, which is crucial during accreditation reviews. Detailed information about funding can be added to the same tab of the sheet. However, we present that information separately in this experiment.
• Aligned with University Identity/Strategic plan/Research plan: Universities often have distinct identities based on their founding principles, such as community engagement, innovation, sustainability, or global impact. Aligning the KPI of funding associated with publications with these values ensures that the research output and funding sources reflect the university’s unique position. For example, if the university prioritizes sustainability, tracking publications and funding in areas like climate research or renewable energy would align with its core identity. By emphasizing research funding in fields that enhance the university’s identity, such as the health sector, social impact, agriculture or technology, the KPI supports building a reputation around the institution’s strengths, which is crucial for accreditation and attracting top faculty, students, and collaborators. Creating categories in dynamic Google Sheets that align with strategic research priorities (e.g., sustainability, digital transformation, and health innovation). When funding or publications are added, they are automatically sorted and tracked within these strategic areas.
• Collaborating university name (if any): Collaboration with other universities, whether at a national or international level, strengthens academic networks and enhances the institution’s reach. This information helps to track which universities the institution/department is working with and assess the value of these partnerships. Publications produced in collaboration with other universities often reflect higher quality due to the pooling of expertise, diverse perspectives, and shared resources. Tracking the universities involved in collaborations allows the institution to monitor the quality of its research partnerships. Collaborating with prestigious or well-ranked institutions can elevate the visibility and impact of research, leading to higher citation rates and broader recognition.
  In the dynamic Google Sheets, creating a specific column or field for Collaborating University next to each publication entry will allow easy tracking of which universities are contributing to each publication. Additional features can also be added as per requirement to generate periodic reports showing the number of collaborative publications with each university, sorted by year, department, or research area. This provides a clear picture of the department’s or college’s collaborative efforts to fulfill the university’s strategic goals.

2.1.3. Training Workshops

Faculty-led training workshops play a crucial role in enhancing both individual and institutional performance. They promote continuous professional development, align with strategic goals, improve teaching and research quality, and contribute to the university’s accreditation success. Tracking and reporting these activities ensures that the institution can demonstrate its commitment to fostering a culture of learning and excellence. A model header for tracking these activities through the Google interactive file is presented in

Table 3. Header for Training and Workshops tab.

Serial No.

Activity year

Activity Month

Activity title

Most suitable category

Overall Rating in 5-point scale (you received as a trainer from participants or your satisfaction score as participant/organizer)

Organized committee/ Deanship/University/Other non-academic body (write name of them)

.

Here, we present complete insights as to the significance of entries in Table 3.

• Tracking faculty members’ roles in professional development activities: Faculty members can participate in professional development activities in multiple capacities—such as participant, trainer/speaker, or organizer. It is crucial to track these roles individually to provide a comprehensive view of their engagement. By distinguishing these roles in the tracking system, one can achieve the following:
  • Gain insights into leadership roles: track how often faculty members are leading as trainers or organizers, not just as participants.
  • Measure diverse engagement: understanding whether a faculty member is more active as a participant or an organizer helps to assess both learning opportunities and contributions to peer development.
  Implementation in Google Sheets: add a column titled Role with a drop-down menu that allows faculty to select their role: (i) Participant, (ii) Trainer/Speaker, (iii) Organizer.
• Categorizing Training and Workshops: Different types of training and workshops may or may not be relevant to specific academic programs or departments. To ensure that faculty development activities align with departmental needs, it is important to categorize training by its focus. This helps program-level committees and faculty members plan activities that directly benefit their program’s goals. Implementation:
  • Create a column titled Category with a drop-down menu of relevant categories, as follows:

    –

    Teaching and Learning;

    –

    Curriculum Development;

    –

    Career Guidance;

    –

    Science and Technology;

    –

    Data Science;

    –

    Software;

    –

    Machine Learning;

    –

    Artificial Intelligence;

    –

    Other (with an option to specify).

  • This will allow quick filtering and analysis of training relevance to the program or department, making it easier to track targeted professional development.
• Providing Feedback on Training Programs: Constructive feedback is essential for improving the quality of training programs. Organizers can create feedback surveys, using tools like Google Forms, to gather input from participants on the effectiveness of the training. While a full analysis of survey results could be a separate task, a general sense of satisfaction can be captured in the tracking sheet to provide an immediate overview of training effectiveness. Implementation:
  • Add a column titled Satisfaction Rating where faculty can input the average satisfaction rating collected from attendees or their own rating on a 5-point scale. This gives an easy-to-track measure of overall success.
• Tracking Training Program Organizers It is valuable to distinguish between internal training activities conducted by the university and external programs hosted by other institutions or organizations. Tracking the name of the organizer provides insight into how faculty members are engaging with both internal and external professional development opportunities. Implementation:
  • Add a column titled Organizer name to capture the name of the organizing body (e.g., internal university departments, external professional organizations). This helps in evaluating the diversity and source of professional development activities.

2.1.4. Research Funding

Including research funding in the KPI measurement sheet is critical for tracking the institution’s/department’s research success and financial health. It can be fully automated in a dynamic Google Sheet by leveraging formulas, visualizations, and automated data inputs to reduce manual work and ensure real-time updates. A typical header for the sheet is given in

Table 4. Header for Research Funding tab.

Serial No.

Contract starting year

Contract starting Month

No. department researcher

No. university researcher

No. researcher outside university

Your Role

Funding track/any details

Funding agency

Current status:

.

By knowing the contract year and month of the funding, one can obtain an idea about the funding activities in a time period. The main role of the department’s faculty member can be identified from the cell mentioning your role, where a drop-down menu can be added with (i) Principal Investigator, (ii) Co-investigator, and (iii) Thesis supervisor in the case of student track projects, and (iv) Advisor in the case of external fundings, etc. Funding agencies can be categorized as (i) Internal, (ii) External university, (iii) External-Non-academic agencies, and (iv) Ministry or any other funding agency (in the specific country or region).

2.1.5. Scientific Seminars

Scientific seminars are significant as a key performance indicator (KPI) because they reflect the department’s or college’s commitment to academic engagement and intellectual growth. Tracking the number and quality of seminars can assess faculty and student participation in research dissemination and collaboration. It also measures the institution’s ability to attract external experts, which enhances its reputation and provides networking opportunities for future research and development. This KPI helps ensure that the academic community remains active, dynamic, and well-connected with the broader research ecosystem. A typical header for the scientific seminar tab is presented in

Table 5. Header for Scientific Seminar tab.

Serial No.

Year

Month

Presenter rank

Seminar title/topic

Your role

.

2.1.6. Thesis Guidance

The significance of thesis guiding as a domain to measure a key performance indicator (KPI) stems from the fact that it reflects the function of the academic institution in guiding and assisting students with their research. An understanding of the faculty’s role in student growth can be gained by keeping track of the quantity of theses that are advised by them, as well as their caliber and completion rates. The institution’s dedication to research output, academic excellence, and preparing students for advanced professional or academic careers are also highlighted by this KPI. It also aids in evaluating the general efficacy of mentorship programs and the degree to which professor competence and student research interests coincide. We may measure multiple KPIs with the aid of the information collected using

Table 6. Header for Thesis Guidance tab.

Serial No.

Enrolled year

Enrolled Semester

Completion year

Completion Semester

Supervising role

Student name

Thesis details

Publication details

Funding

.

2.1.7. Conferences

In academic settings, conferences are a powerful tool for tracking key performance indicators (KPIs) since they show how involved the institution is with the larger research community. The frequency of presentations and papers given, the number of conferences that staff and students attend, and the number of conferences that the institution hosts itself are examples of important indicators. These KPIs evaluate cooperation, the visibility of the institution’s contributions on a national and worldwide scale, and the dissemination of research. They also assess how well professional growth, networking, and the institution’s influence are operating in particular fields of study. The required information can be collected through

Table 7. Header for Conference tab.

Serial No.

Year

Month

Conference details

Your role

Support received (give a detailed note)

.

2.1.8. Undergraduate Project Guidance

Tracking undergraduate project guidance as a KPI in the Google Sheet provides a structured approach to measuring faculty involvement in student mentorship, ensuring alignment with program goals, and meeting accreditation standards. By capturing key elements (as in

Table 8. Header for Undergraduate Project Guidance tab.

Serial No.

Academic year

Semester

Name of student(s)

Project title

Project type

Software and Technical skill learned/implemented

Remark or any other information

) like project details, and outcomes, it is possible to generate valuable insights into the institution’s overall student engagement, research capacity, and program success.

2.1.9. Citation

Tracking citations of publications as a KPI in Google Sheets provides a clear measure of research impact, helps align faculty research with institutional goals, and supports faculty development and recognition. By organizing key metrics (as in

Table 9. Header for Citation tab.

Serial No.

Citation year

Google Scholar citation

Scopus

) like total citations, citation sources, and publication details, the author can create a structured way to monitor and report on the research influence of faculty members, which is critical for accreditation, rankings, and strategic planning. Regular updates and analyses of citation data can drive research priorities, recognize high-impact work, and enhance the university’s reputation in the academic community.

2.1.10. Review Activities

Tracking review activities such as journal article reviews, thesis evaluations, and faculty promotion participation is a critical part of measuring faculty service contributions in a KPI system. By organizing key data points(as in

Table 10. Header for Review Activities tab.

Serial No.

Review year

Review Month

Category

Write possible shareable information (like article title, journal name, etc.)

In case of thesis

Mention the name of external department/ college/university

) like review type, journal or institution name, and the number of reviews, it is possible to gain valuable insights into the academic service efforts of faculty members. These data support workload management, faculty recognition, and accreditation requirements while highlighting the institution’s role in maintaining academic standards across various disciplines.

2.1.11. Rewards and Patents

Tracking rewards and patents in the KPI Google Sheets is an essential way to measure faculty achievements, highlight innovation, and support institutional goals around research impact and academic excellence. By organizing key metrics like faculty name, reward details, patent status, and commercialization efforts, institutions can effectively monitor and showcase their contributions to both academic and industry advancements (

Table 11. Header for Rewards and Patents tab.

Serial No.

Rewards/Patents year

Month

Details about Reward/Patents

Name of Organization/ University with country

Category/Field of rewards/Patents

). Regularly updating these data provides valuable insights for strategic planning, faculty evaluations, and accreditation processes, fostering a culture of recognition and innovation.

2.1.12. Academic Advisory

Tracking academic advising as a KPI in a Google Sheet provides valuable insights into faculty engagement, student support, and institutional effectiveness in guiding students through their academic journeys. By organizing key metrics such as the number of advisees, types of advising, and outcomes, institutions can ensure that students are receiving adequate and personalized support while also recognizing faculty contributions to this critical area. Regular updates and clear categorization will help improve resource allocation, support accreditation efforts, and ultimately enhance both student success and faculty evaluation processes. In this study, we mainly aim to find the completion rate and first-year retention rate. A sample header for this purpose is presented in

Table 12. Header for Academic Advisory tab.

Serial No.

Students Name

Students Id

Gender

Entering year in program

Current Credit earned (please update in each semester)

Current status

Status Month

Status Year

. By tracking the entry year and graduation year, the program can locally monitor the completion rate in real time without waiting for the data from the university IT center or admission and registration unit.

2.2. Combination of Individual Data for Further Utilization

Our aim here is to accumulate several years of data in a single place to track KPIs related to professional development and activities. For this purpose, we have to import all twelve tabs’ information from 30 male and 25 female faculty’s individual portfolios to a single sheet in twelve different tabs. We can also create twelve different sheets and import the information of faculty’s individual portfolios from the twelve tabs to all those sheets. To import the data, we will use =IMPORTRANGE("link","tab name"!range).

Importing Data of Tab 2—Research Publications

Follow the below steps to import the data of tab 2—Research publications from all individual faculty portfolios:

1.

Create a new Google Sheet with the name Combined record of research publications and add a tab name Publication_records.

2.

Set the header of the tab Publication_records as shown in Table 2 from column C to N, and set Column A and B by faculty name and gender, respectively.

3.

Import the required range from Faculty_1’s portfolio by using the following code,

=IMPORTRANGE("link of faculty1 profile","2-Research publications!A3:     N102")

in the cell $C2$. Please note, we fixed one hundred rows for each faculty member. The monitoring authority can be informed if any faculty has published more than one hundred articles to extend the number of rows for the concerned faculty.

4.

After completion of the above three steps, the sheet will look as shown in Figure 3.

5.

By clicking on Allow Access, the data of faculty_1’s research publication will be imported. This is an automated system, and whenever faculty_1 updates any record, this sheet will be updated in real time, as shown in Figure 4.

6.

Repeat step 4 and step 5 for faculty_2 on row 102. Then, the sheet will be structured as in Figure 5. Continue the process until it is linked with all 30 male and 25 female faculty members. We would like to remark here that there will be lots of empty rows between the records of the two faculties.

7.

After importing all data in a single sheet, name it as Publication_records; we can formulate mechanisms to utilize the data for different purposes, including KPI calculation. We elaborate on this in the next step.

The process of utilizing collected data on faculty research publications is tailored to meet the specific requirements of the program, department, university, or any relevant authority. Currently, we have a total of $(100\times 55)+1=5501$ rows, where some entries are completed for each faculty member, while others remain unfilled.

Our aim here is to filter publication data for the year 2024 as a sample of our experiment.

2.3. Filtering Research Publication Information Based on Required Criteria

Currently, we have a total of $(100\times 55)+1=5501$ rows, where some entries are completed for each faculty member, while others remain unfilled. Our primary aim here is to filter data for the year 2024 as a sample of our experiment. But, applying similar code, the information can be filtered for any specific duration.

In this part, we are going to explain how to extract different information based on criteria. Please note, we are not intended to calculate the number here. To have a criteria based on clean data, we will create a new sheet beside Publication_records by the name Filtered_information and use the formula

=FILTER(range, condition1, [condition2,...])

The complete process is listed below:

• Detailed list of all publications in a year: Assume that the list of publications in the calendar year 2024 needs to be filtered. This can be obtained in the sheet Filtered_information using the following formula:

  =Filter(Publication_records!A2:N21500, Publication_records!D2:D21500     =2024)

  The range $A2:N21500$ can be modified such that it encompasses all of the information in the sheet Publication_records. The year of publication is indicated in the range $D2:D21500$. The publishing information for the calendar year 2025 can be filtered by substituting $D2:D21500=2025$ for $D2:D21500=2024$.
• A comprehensive list of all publications across a number of years: In the case where data are required for three years—namely, 2023, 2024, and 2025—we can use the following filtered formula:

  =Filter(Publication_records!A2:N21500, (Publication_records!D2:D21500     =2023)+(Publication_records!D2:D21500=2024)+(Publication_records!     D2:D21500=2025))

• List of all publications in $Q_1$ ranked journal in a year: List of publications in $Q_1$ ranked journal for the year can be filtered by

  =Filter(Publication_records!A2:N21500, Publication_records!D2:D21500     =2024, Publication_records!K2:K21500=Q1)

  In case the requirement is for years 2023, 2024, and 2025, the code can be

  =Filter(Publication_records!A2:N21500, (Publication_records!D2:D21500     =2023)+(Publication_records!D2:D21500=2024)+(Publication_records!     D2:D21500=2025), Publication_records!K2:K21500=Q1)

• List of all single-author publications in a year: We must select department author as one and other authors will be zero in the header filter in order to monitor just single-author articles. Therefore, the appropriate filtering code is

  =Filter(Publication_records!A2:N21500, Publication_records!D2:D21500     =2024, Publication_records!F2:F21500=1,Publication_records!G2:     G21500=0, Publication_records!H2:H21501=0)

• Group research publication within department: To know the group activities among faculty members within the department, we have to filter the publication record by

  =Filter(Publication_records!A2:N21500, Publication_records!D2:D21500     =2024, Publication_records!F2:F21500>=1,Publication_records!G2:     G21500=0, Publication_records!H2:H21501=0)

• Interdisciplinary research publication within university: By interdisciplinary research, we mean a department member or a research group collaborating with a member or a group from another department within the college or from other colleges. The outcome of such a collaboration can be accessed through the code

  =Filter(Publication_records!A2:N21500, Publication_records!D2:D21500     =2024, Publication_records!F2:F21500>=1,Publication_records!G2:     G21500>=1, Publication_records!H2:H21501=0)

• Collaboration with national and international researchers: This collaboration may be undertaken through individual efforts by academic members or by a formal memorandum of understanding (MoU) between colleges, universities, or programs. The outcomes can be filtered through

  =Filter(Publication_records!A2:N21500, Publication_records!D2:D21500     =2024, Publication_records!F2:F21500>=1,Publication_records!G2:     G21500>=1, Publication_records!H2:H21501>=1)

• Publication in ranked journal: To monitor a specific rank, say $Q_1$, of journals where faculty members are publishing in a year can be filtered through

  Filter(Publication_records!A2:N21501, Publication_records!D2:D21501     =2024, Publication_records!K2:K21501="Q1")

  We will have two choices based on the drop-down menu if the query is for any ranking journal. In our instance, selections are available in the corresponding drop-down menu.

  $$\left(i\right)Q_1\left(ii\right)Q_2\left(iii\right)Q_3\left(iv\right)Q_4\left(v\right)\mathrm{Conf}-\mathrm{Procd}-\mathrm{SCI}/\mathrm{Scopus}.$$
  In this case, the running code is

  Filter(Publication_records!A2:N21501, Publication_records!D2:D21501     =2024, Publication_records!K2:K21501<>"Conf-Procd-SCI/Scopus")

  In other cases, the filtering process can also be carried out using

  =Filter(Publication_records!A2:N21501, Publication_records!D2:D21501     =2024, (Publication_records!     K2:K21501="Q1")+(Publication_records!K2:K21501="Q2")+(     Publication_records!K2:K21501="Q3")+(Publication_records!K2:K21501="Q4"))

• Publications that are aligned with any funding: Funded publication by any funding agency can be tracked through

  Filter(Publication_records!A2:N21501, Publication_records!D2:D21501     =2024, Publication_records!L2:L21501<>"None")

  To obtain details about the support of a particular funding agency, then use

  =Filter(Publication_records!A2:N21501, Publication_records!D2:D21501     =2024, Publication_records!L2:L21501="name of funding agency")

  In order to prevent errors caused by different faculty members entering different abbreviations, it is preferable to build a drop-down menu.
• Publications that are aligned with university identity or strategic plan activities: Generally, the statement of university identity or the required category of strategic plan activities are well known in advance. Hence, a drop-down menu can be created easily. If we need to know about publications that align with any of the identities, then use

  =Filter(Publication_records!A2:N21501, Publication_records!D2:D21501     =2024, Publication_records!M2:M21501<>"None")

  Finally, if we need data for a specific identity, such as Health, then use

  =Filter(Publication_records!A2:N21500, Publication_records!D2:D21500     =2024, Publication_records!M2:M21500="Health")

2.4. Filtering Training and Workshop Events According to Their Type

• Detailed list of all training programs attended or organized by department members

  =Filter(Training_records!A2:N21500, Training_records!D2:D21500     =2024)

• Detailed list of all training programs attended by department members

  =Filter(Trainig_Record!A2:N21501, Trainig_Record!D2:D21500=2024,     Trainig_Record!F2:F21500="Participant")

• Detailed list of all training programs organized or trained by department members In this case, we have to filter based on the category of options: Your role. In our system, we provide options to choose from (i) trainer/speaker; (ii) self-organized; and (iii) organizer as a committee task. Members have the option to choose more than one option if applicable. Hence, we have the following options to filter:

  =Filter(Trainig_Record!A2:N21501, Trainig_Record!D2:D21501=2024, (     Trainig_Record!F2:F21501="Trainer/speaker, Organizer as a      committee task")+(Trainig_Record!F2:F21501="Organizer as a     committee task")+(Trainig_Record!F2:F21501="Trainer/speaker")+(     Trainig_Record!F2:F21501="Self Organized") +(Trainig_Record!F2:     F21501="Trainer/speaker, Self Organized"))

• Engagement in a particular kind of training: Suppose that we would like to filter details of a certain kind of training activity, for example, software and programming. Then, we can use the following code:

  =Filter(Trainig_Record!A2:N21501, Trainig_Record!D2:D21501=2024,     Trainig_Record!H2:H21501="Software and programming")

  If we want to limit our search about the training programs organized by department members as a trainer/speaker or organized as a committee task, we have to use

  =Filter(Trainig_Record!A2:N21501, Trainig_Record!D2:D21501=2024,     Trainig_Record!H2:H21501="Software and programming",(     Trainig_Record!F2:F21501="Trainer/speaker")+(Trainig_Record!F2:     F21501="Trainer/speaker, Organizer as a committee task")+(     Trainig_Record!F2:F21501="Organizer as a committee task")+(     Trainig_Record!F2:F21501="Self Organized") )

In the same manner, we can filter the information as per requirements. We omit further details.

2.5. Filtering Information Related to Research Funding

We can use a filter to customize information about research funding. we show some of them here.

• Information about internal funding

  =Filter(Research_Funding!A2:N21501, Research_Funding!D2:D21501=2024,     Research_Funding!L2:L21501="Internal")

• Information about external funding

  =Filter(Research_Funding!A2:N21501, Research_Funding!D2:D21501=2024, (     Research_Funding!L2:L21501="External- National University")+(     Research_Funding!L2:L21501="External- Ministry or Goverment body")+(     Research_Funding!L2:L21501="External -International University")+(     Research_Funding!L2:L21501="External- Private organization"))

  If there is a requirement about funding information for any single external category, then the last entry can be relaxed accordingly.
• Information about funding with students

  =Filter(Research_Funding!A2:O21501, Research_Funding!D2:D21501=2024, (     Research_Funding!O2:O21501="Master")+(Research_Funding!O2:O21501="     Bachelor")+(Research_Funding!O2:O21501="Doctoral"))

  If there is a requirement for student in any specific program like a Bachelor, the filtering process can be simplified as follows:

  =Filter(Research_Funding!A2:O21501, Research_Funding!D2:D21501=2024,     Research_Funding!O2:O21501="Master")

2.6. Filtering Information Related to Scientific Seminar

Here, the data can be filtered by factors like the total number of scientific seminars held in a certain year or academic year, or the number of seminars given by students or professors. Information on individuals attending student seminars, for example, can be collected by

=Filter(Scientific_Seminer!A2:N21501, Scientific_Seminer!D2:D21501=2024,     Scientific_Seminer!F2:F21501="Master student")

This formulation will highlight the faculty member’s consistent participation in student seminars, which further reflects their commitment to assisting student-level research. Faculty members also assist students in developing their presentation and public speaking skills by participating in Q&A sessions.

Notice here that the combined record of Scientific_Seminar includes all information about seminars attended by an individual faculty member. Thus, there will be multiple entries of the title of a seminar. Next, a unique seminar presented by students in 2024 can be listed through

=Unique(FILTER(Scientific_Seminer!G2:G21500, (Scientific_Seminer!G2:G21500     <>"")*(Scientific_Seminer!D2:D21500=2024)*(Scientific_Seminer!F2:F21501="Master student")))

We note here that this will return only one column with the unique seminar title. This is helpful for counting the number of seminars presented by a student.

Remark 1.

To restrict the length of the article, we avoid mentioning the filtering process of other information. We will use those tabs to calculate relevant KPIs in the next section.

3. Direct Calculation of Key Performance Indicators

3.1. Domain: Research Publications

In this part of the article, we present some code that directly calculates the KPI from the filled dataset. To validate the code, we create a dataset of ten faculty members’ research publications, as presented in

Table 13. Faculty publications and related information.

	A	B	C	D	E	F	J	H	I	J	K	L	M
	Faculty Name	Gender	Serial No.	Published Year	Published Month	No. Dept Author	No. Univ Author	No. External Authors	Article Details	DOI	Journal Rank	Funded by	Aligned with Univ Identity
1	Faculty_1	Male	1	2023	February	1	0	1	IF_1, OF_1, first article of faculty_1, journal name, volume, page no	DOI-IF_1	Q1	Scientific council	Energy
2	Faculty_1	Male	2	2024	January	2	0	0	IF_1, IF_3, A joint article of faculty 1 and faculty 3, journal name, volume, page no	DOI-IF_1-IF_3	Q2	None	None
3	Faculty_1	Male	3	2024	March	2	0	1	IF_7, IF_1, OF_2, A joint article of faculty_1, journal name, volume, page no	DOI-IF_7-IF_1	Q1	Scientific council	Energy
4	Faculty_1	Male	4	2024	October	1	0	0	IF_1, first article of faculty_1, journal name, volume, page no	DOI-IF_1-2	Q1	Scientific council	Energy
5	Faculty_1	Male	5	2024	November	1	1	1	IF_1, UF_1, OF_3, A joint article of faculty_1, journal name, volume, page no	DOI-IF_1-3	Conf-Procd- SCI/Scopus	None	None
6	Faculty_2	Male	1	2023	April	1	0	0	IF_2, first article of faculty_2, journal name, volume, page no	DOI-IF_2	Q2	Ministry	Health
7	Faculty_2	Male	2	2024	February	1	0	0	IF_2, second article of faculty_2, journal name, volume, page no	DOI-IF_2-2	Q3	None	None
8	Faculty_2	Male	3	2024	October	1	0	0	IF_2, third article of faculty_2, journal name, volume, page no	DOI-IF_2-3	Q1	Ministry	None
9	Faculty_2	Male	4	2025	January	1	0	0	IF_2, fourth article of faculty_2, journal name, volume, page no	DOI-IF_2-4	Q1	Ministry	Health
10	Faculty_3	Male	1	2024	April	1	1	0	IF_3, first article of faculty_3, journal name, volume, page no	DOI-IF_3	Q2	Scientific council	Health
11	Faculty_3	Male	2	2024	January	2	0	0	IF_1, IF_3, A joint article of faculty 1 and faculty 3, journal name, volume, page no	DOI-IF_1-IF_3	Q2	None	None
12	Faculty_3	Male	3	2024	October	2	0	0	IF_3, IF_9, OF_2, A joint article of faculty_3 and faculty_9, journal name, volume, page no	DOI-IF_3-IF_9	Q1	None	None
13	Faculty_3	Male	4	2024	October	1	0	1	IF_3, OF_3, an article of faculty_3 with outside university faculty, journal name, volume, page no	DOI-IF_3-2	Q1	Scientific council	Health
14	Faculty_4	Male	1	2025	January	1	0	0	IF_4, first article of faculty_4, journal name, volume, page no	DOI-IF_4	Q4	None	None
15	Faculty_5	Male	1	2023	February	1	0	0	IF_5, first article of faculty_5, journal name, volume, page no	DOI-IF_5	Q1	Scientific council	Energy
16	Faculty_5	Male	2	2024	January	1	0	0	IF_5, second article of faculty_5, journal name, volume, page no	DOI-IF_5_2	Q2	None	None
17	Faculty_5	Male	3	2024	March	2	0	0	IF_5, third article of faculty_5, journal name, volume, page no	DOI-IF_5_3	Q1	Scientific council	Energy
18	Faculty_5	Male	4	2024	October	1	0	0	IF_5, fourth article of faculty_5, journal name, volume, page no	DOI-IF_5_4	Q1	Scientific council	Energy
19	Faculty_6	Male	1	2024	January	2	0	0	IF_6 and IF_10 a joint article by faculty_6 and faculty_10, journal name, volume, page no	DOI-IF_6	Q1	Scientific council	Health
20	Faculty_6	Male	2	2024	July	1	1	0	IF_5 and UF, an article of faculty_6 jointly with other department faculty, journal name, volume, page no	DOI-IF_6_2	Q3	None	None
21	Faculty_7	Male	1	2024	March	2	0	1	IF_7, IF_1, OF_2, A joint article of faculty_1, journal name, volume, page no	DOI-IF_7-IF_1	Q1	Scientific council	Energy
22	Faculty_8	Female	1	2024	July	1	0	0	IF_8, an article by faculty_8, journal name, volume, page no	DOI-IF_8	Q3	Scientific council	Health
23	Faculty_9	Female	1	2024	October	2	0	0	IF_3, IF_9, OF_2, A joint article of faculty_3 and faculty_9, journal name, volume, page no	DOI-IF_3-IF_9	Q2	None	None
24	Faculty_9	Female	2	2025	January	1	0	0	IF_10 and IF_9, a joint article by faculty_10 and faculty_9, journal name, volume, page no	DOI-IF_10_3	Q1	Scientific council	Energy
25	Faculty_10	Female	1	2024	January	2	0	0	IF_6 and IF_10, a joint article by faculty_6 and faculty_10, journal name, volume, page no	DOI-IF_6	Q1	Scientific council	Health
26	Faculty_10	Female	2	2024	May	1	0	1	OF and IF_10, an article by faculty_10 with outside faculty, journal name, volume, page no	DOI-IF_10_1	Q2	None	None
27	Faculty_10	Female	3	2024	December	1	0	0	IF_10, first article by faculty_10, journal name, volume, page no	DOI-IF_10_2	Q1	Scientific council	Energy

. This table is obtained by using the method for combining the records mentioned in Section 2.2.

Next, we are going to present some mechanisms to measure KPIs related to scientific research using data from Table 13. Before that, let us understand the dataset in Table 13: out of 10 faculties, seven are male and three are female. The given dataset indicates that from year 2023 until 2025, all faculty members have at least one publication. The following abbreviations are used:

• IF: Internal faculty, i.e., a faculty member from the department or program under study.
• UF: University faculty, i.e., a faculty member from the same university but in another department or college other than the department or program under study.
• OF: Outside faculty, i.e., a faculty member from another national or international university.

1.

KPI Statement: Total number of research article publications by departments’ faculty members. This KPI is intended to measure overall publication records by the department faculty. The total number of publications can be counted by the following code:

=COUNTA(UNIQUE(FILTER(Publication_records!J2:J2100,(     Publication_records!J2:J2100<>"")*(Publication_records!D2:D2100=2024))))

It can be observed that faculty members have some group publications with department members. This code counts those articles one time only and gives an exact physical count of published articles.

Let us validate this code using data from Table 13. There are a total of twenty-eight (28) publications by ten faculty members. We are searching for records of the year 2024 only; we have 21 publications. Further, collaborative research among faculty members within the department results in duplication of DOIs as follows: $J3=J12$; $J4=J22$; $J13=J24$; $J20=J26$. Thus, the total number of unique publications in the department by the ten faculty members in the year 2024 is seventeen. This validation through the code is presented in Figure 6.

2.

KPI Statement: Percentage of faculty members with at least one publication in a year. This KPI provides the percentage of faculty members with at least one publication in the year 2024. It is evident from Table 13 that Faculty-4 does not have any publication in 2024. Thus, out of ten faculty members, nine have at least one publication in 2024. This calculation is validated in Figure 7 and directly calculated from Google Sheets as follows:

=COUNTA(UNIQUE(FILTER(Publication_records!A2:A2100,(     Publication_records!J2:J2100<>"")*(Publication_records!D2:D2100     =2024))))*(100/(Number of faculty members of rank Assistant professor and above))

3.

KPI Statement: Average publication rate in a year. We aim here to calculate the average rate of publications in the year 2024. We calculate this by adding all publications by each faculty members divided by the number of faculty in the department.

=COUNTA(Publication_records!D2:D2100=2024)/(Number of faculty members     of rank Assistant professor and above)

This is the easiest one among all KPIs calculations. In 2024, there are a total of 21 publications and 10 faculty members. Thus, the average is equal to $2.1$, which is validated in Figure 8 by using code.

4.

KPI Statement: Number of funded research publications. This KPI will provide the impact of funding in scientific publications. From column L of Table 13, one can observe that there are twelve articles in 2024 that are funded by different sources. One article is funded by the Ministry, while eleven articles are funded by the Scientific Council. The following code gives the total number of publications:

=COUNTIFS(Publication_records!D2:D21501,2024, Publication_records!L$2:     L$21501, "<>") - COUNTIFS(Publication_records!D2:D21501,2024,     Publication_records!L$2:L$21501, "None")

In case the requirement is to know the number of publications funded by a particular agency, namely, scientific council, it is derived by

=COUNTIFS(Publication_records!D2:D21501,2024, Publication_records!L$2:     L$21501, "scientific council")

Both codes are presented in Figure 9.

We remark here that to have accurate data, all the publications should be associated with the name of a funding resource or need to be filled as “None” in column L.

5.

KPI Statement: Total number of publications aligned with university identity. This KPI will provide the impact of funding in scientific publications.

=COUNTIFS(Publication_records!D2:D21501, 2024, Publication_records!M$2     :M$21501, "<>") - COUNTIFS(Publication_records!D2:D21501, 2024,     Publication_records!M$2:M$21501, "None")

A drop-down menu with the list of identities and the option for choosing “None” will be helpful to have the correct set of data. The drop-down menu is also helpful for calculating the number of publications in a specific identity, namely, “Health”. This can be calculated by using  

=COUNTIFS(Publication_records!D2:D21501,2024, Publication_records!M$2:     M$21501, "Health")

The validation of this KPI is similar to the above KPI about the funding; hence, we do not include it here as a Google Sheet image.

6.

KPI Statement: Number of publications in $Q_i$ Journal. The Kth column of Table 13 indicates that there are $Q_i$, $i=2,3,4$ as follows: $Q_1=11$, $Q_2=6$, $Q_3=3$, and $Q_4=0$. To automate this calculation, one can use  

=COUNTIFS(Publication_records!D2:D21501, 2024, Publication_records!K$2     :K$21501, "Q1")

which is validated in Figure 10.

Similarly, the number of publications in $Q_2$, $Q_3$, and $Q_4$ can be counted by  

=COUNTIFS(Publication_records!D2:D21501, 2024, Publication_records!K$2     :K$21501, "Qi")

by replacing $i=2,3,4$ in $Q_i$, respectively.

3.2. Domain: Training and Workshops

There are numerous KPI statements that might be made in this area. A small number of KPIs that we specify and track might be appropriate for any accrediting process.

• KPI Statement: Number of faculty members actively participating in professional development activities. We will measure this KPI in the sense of faculty members’ involvement in at least one activity during a time frame in the capacity of (i) participation, (ii) trainer/speaker, (iii) organizer as committee task, and (iv) self-organized.

  =Counta(Unique(FILTER(Trainig_Record!A2:A21500, (Trainig_Record!F2:     F21500<>"")*(Trainig_Record!D2:D21500=2024))))

• KPI Statement: Number of activities related to software and programming.

  =Counta(Unique(FILTER(Trainig_Record!G2:G21500, (Trainig_Record!G2:     G21500<>"")*(Trainig_Record!D2:D21500=2024)*(Trainig_Record!H2:     H21500="Software and programming")))

• KPI Statement: Number of activities related to Teaching and Learning.

  =Counta(Unique(FILTER(Trainig_Record!G2:G21500, (Trainig_Record!G2:     G21500<>"")*(Trainig_Record!D2:D21500=2024)*(Trainig_Record!H2:     H21500="Teaching and Learning")))

Remark 2.

For KPIs related to the number of activities in any category, replace

Trainig_Record!H2:H21500="Teaching and Learning")by the name of the category as

Trainig_Record!H2:H21500="Name of category").

To count the unique number, we have to use the unique filter function on the title of the activity. Since the title information is filled by faculty members, a single comma may count it as a unique entry. Hence, those measuring this kind of KPI need to be careful. They may process it by filtering complete information of related activities and recount it separately as follows:  

=Unique(FILTER(Trainig_Record!G2:G21500, (Trainig_Record!G2:G21500<>"")*(Trainig_Record!D2:D21500=2024)*(     Trainig_Record!H2:H21500="Teaching and Learning")))

3.3. Domain: Research Funding

• KPI Statement: Number of internal funds.

  =Countifs(Research_Funding!D2:D21501, 2024, Research_Funding!L2:L21501     , "internal")

• KPI Statement: Number of external funds.

  =Countifs(Research_Funding!D2:D21501, 2024, Research_Funding!L2:L21501     , "*External*")

  This will provide all external funds’ information. If details of any specific external funding are needed, replace "*External*" by "*Name of external funding from drop-down*".
• KPI Statement: Number of faculty members with at least one fund.

  =Counta(Unique(Filter(Research_Funding!A2:A21500,(Research_Funding!D2:     D21500=2024)*(Research_Funding!L2     :L21500<>"") )))

  If the KPI is used to calculate a percentage rather than a number, it can be divided by the total number of faculty members.
• KPI Statement: Number of female faculty members with at least one fund.

  =Counta(Unique(Filter(Research_Funding!A2:A21500,(Research_Funding!D2:     D21500=2024)*(Research_Funding!L2:L21500<>"")*(Research_Funding!L2     :L21500="Female") )))

  If the KPI is used to calculate a percentage rather than a number, it can be divided by the total number of female faculty members.
• KPI Statement: Number of male faculty members with at least one fund.

  =Counta(Unique(Filter(Research_Funding!A2:A21500,(Research_Funding!D2:     D21500=2024)*(Research_Funding!L2:L21500<>"")*(Research_Funding!L2     :L21500="male") )))

  If the KPI is used to calculate a percentage rather than a number, it can be divided by the total number of male faculty members.

3.4. Domain: Scientific Seminar

We restrict ourselves to the assessment of following KPIs related to the scientific seminar. All of the KPIs can be automatically measured by using two conditions, namely, uniqueness of the seminar title and presenter rank.

• KPI Statement: Number of scientific seminars presented by a master’s student.

  =COUNTA(Unique(FILTER(Scientific_Seminer!G2:G21500, (     Scientific_Seminer!G2:G21500<>"")*(Scientific_Seminer!D2:D21500     =2024)*(Scientific_Seminer!F2:F21501="Master student"))))

• KPI Statement: Number of scientific seminars presented by a doctorate student.

  =COUNTA(Unique(FILTER(Scientific_Seminer!G2:G21500, (     Scientific_Seminer!G2:G21500<>"")*(Scientific_Seminer!D2:D21500     =2024)*(Scientific_Seminer!F2:F21501="Doctorate student"))))

• KPI Statement: Number of scientific seminars presented by a faculty member.

  =COUNTA(Unique(FILTER(Scientific_Seminer!G2:G21500, (     Scientific_Seminer!G2:G21500<>"")*(Scientific_Seminer!D2:D21500     =2024)*(Scientific_Seminer!F2:F21501="Faculty- department"))))

• KPI Statement: Number of scientific seminars presented by an external researcher.

  =COUNTA(Unique(FILTER(Scientific_Seminer!G2:G21500, (     Scientific_Seminer!G2:G21500<>"")*(Scientific_Seminer!D2:D21500     =2024)*(Scientific_Seminer!F2:F21501="External researcher"))))

• KPI Statement: Number of faculty members who attended at least one scientific seminar.

  = Counta(Unique(FILTER(Scientific_Seminer!A2:A21500, (Scientific_Seminer!G2:G21500<>"")*(     Scientific_Seminer!D2:D21500=2024))))

3.5. Domain: Thesis Guidance

• KPI Statement: Number of students who enrolled in a master’s thesis in the year.

  =Countifs(Thesis_Guidence!D2:D22000, 2024, Thesis_Guidence!H2:H22000,      "Master")

• KPI Statement: Number of students who completed a master’s thesis in the year.

  =Countifs(Thesis_Guidence!F2:F22000, 2024, Thesis_Guidence!H2:H22000,      "Master")

• KPI Statement: Number of students who enrolled in a doctoral thesis in the year.

  =Countifs(Thesis_Guidence!D2:D22000, 2024, Thesis_Guidence!H2:H22000,      "Doctoral")

• KPI Statement: Number of students who completed a doctoral thesis in the year.

  =Countifs(Thesis_Guidence!F2:F22000, 2024, Thesis_Guidence!H2:H22000,      "Doctoral")

• KPI Statement: Number of faculties involved in at least one thesis guidance in a master’s program in the year.

  =COUNTA(Unique(Filter(Thesis_Guidence!A2:A21501, (Thesis_Guidence!F2:     F21501=2024)*(Thesis_Guidence!H2:H21501="Master")*(Thesis_Guidence     !I2:I21501<>" "))))+COUNTA(Unique(Filter(Thesis_Guidence!A2:A21501     , (Thesis_Guidence!D2:D21501=2024)*(Thesis_Guidence!H2:H21501="Master")*(Thesis_Guidence!I2:I21501<>" "))))

• KPI Statement: Number of faculties involved in at least one thesis guidance in a doctoral program in the year.

  =COUNTA(Unique(Filter(Thesis_Guidence!A2:A21501, (Thesis_Guidence!F2:     F21501=2024)*(Thesis_Guidence!H2:H21501="Doctoral")*(     Thesis_Guidence!I2:I21501<>" "))))+COUNTA(Unique(Filter(     Thesis_Guidence!A2:A21501, (Thesis_Guidence!D2:D21501=2024)*(     Thesis_Guidence!H2:H21501="Doctoral")*(Thesis_Guidence!I2:I21501<>" "))))

3.6. Domain: Conference

• KPI Statement: Number of faculty members who attended at least one conference.

  =COUNTA(Unique(Filter(Conference!A2:A21501, (Conference!D2:D21501=     2024)*(Conference!G2:G21501<>" "))))

• KPI Statement: Number of faculty members obtained support for attending a conference.

  =COUNTA(Unique(Filter(Conference!A2:A21501, (Conference!D2:D21501     =2024)*(Conference!H2:H21501<>"Self Sponsered"))))

  Here, support refers to the sponsor.

3.7. Domain: Undergraduate Project

• KPI Statement: Number of projects involving original research.

  =COUNTA(Unique(Filter(UG_Projet!F2:F21501, (UG_Projet!D2:D21501=2024)     *(UG_Projet!H2:H21501="Original Research"))))

• KPI Statement: Number of projects with reviewed research.

  =COUNTA(Unique(Filter(UG_Projet!F2:F21501, (UG_Projet!D2:D21501=2024)     *(UG_Projet!H2:H21501="Review Research"))))

• KPI Statement: Number of project based on software implementations.

  =COUNTA(Unique(Filter(UG_Projet!F2:F21501, (UG_Projet!D2:D21501=2024)     *(UG_Projet!H2:H21501="Software implementation"))))

• KPI Statement: Number of students who learned/implemented LaTeX software.

  =COUNTA(Filter(UG_Projet!I2:I21501, UG_Projet!D2:D21501=2024, Isnumber     (SEARCH("LaTeX", UG_Projet!I2:I21501))))

• KPI Statement: Number of students who learned/implemented Mathematica software.

  =Counta(Filter(UG_Projet!I2:I21501, ISNUMBER(SEARCH("Mathematica",     UG_Projet!I2:I21501))))

• KPI Statement: Number of students who learned/implemented MatLab software.

  =Counta(Filter(UG_Projet!I2:I21501, ISNUMBER(SEARCH("MatLab",      UG_Projet!I2:I21501))))

• KPI Statement: Number of students who learned/implement R programming.

  =Counta(Filter(UG_Projet!I2:I21501, ISNUMBER(SEARCH("R Program",     UG_Projet!I2:I21501))))

3.8. Domain: Citation

• KPI Statement: Total number of citations.

  = Counta(Unique(FILTER(Scientific_Seminer!A2:A21500, (     Scientific_Seminer!G2:G21500<>"")*(Scientific_Seminer!D2:D21500=2024))))

• KPI Statement: Average rate of citation per faculty member.

  = Counta(Unique(FILTER(Scientific_Seminer!A2:A21500, (     Scientific_Seminer!G2:G21500<>"")*(Scientific_Seminer!D2:D21500=2024))))

3.9. Domain: Review Activities

• KPI Statement: Percentage of faculty members who participated in at least one review activity. The following formula will give us the number of faculty members who participated in at least one review activity.

  =COUNTA(Unique(Filter(Review_Activities!A2:A21501, (Review_Activities!     D2:D21501=2024)*(Review_Activities!F2:F21501<>" "))))

  The percentage can be calculated by dividing the total number of faculty-ranked associate professors or above.
• KPI Statement: Total number of journal articles reviewed by faculty members in the year.

  =Countifs(Review_Activities!D2:D22000, 2024, Review_Activities!F2:     F22000, "Journal Article")

• KPI Statement: Total number of internal master theses reviewed by faculty members in the year.

  =Countifs(Review_Activities!D2:D22000, 2024, Review_Activities!F2:     F22000, "Thesis", Review_Activities!H2:H22000, "Master- Department     ")

• KPI Statement: Total number of external master theses reviewed by faculty members in the year.

  =Countifs(Review_Activities!D3:D22001, 2024, Review_Activities!F3:     F22001, "Thesis", Review_Activities!H3:H22001, "Master-External-     KSA")+ Countifs(Review_Activities!D3:D22001, 2024,      Review_Activities!F3:F22001, "Thesis," Review_Activities!H3:H22001, "Master-External-KFU")

• KPI Statement: Total number of internal doctoral theses reviewed by faculty members in the year.

  =Countifs(Review_Activities!D2:D22000, 2024, Review_Activities!F2:     F22000, "Thesis", Review_Activities!H2:H22000, "Doctoral-      Department")

• KPI Statement: Total number of external doctoral theses reviewed by faculty members in the year.

  =Countifs(Review_Activities!D3:D22001, 2024, Review_Activities!F3:     F22001, "Thesis", Review_Activities!H3:H22001, "Master-External-     KSA")+ Countifs(Review_Activities!D3:D22001, 2024,      Review_Activities!F3:F22001, "Thesis," Review_Activities!H3:H22001     , "Master-External-KFU")

Remark 3.

Faculty members’ involvement in external thesis reviews reflects the department’s research depth and influence, underscoring its expertise and scholarly reputation. This role not only improves collaboration but also brings fresh insights that enrich the department’s academic community.

In this sense, a suitable KPI statement might be as follows:“Number of faculty members who reviewed at least one external master’s or doctoral thesis within a specified time frame”.

Including reviews of promotion files from other universities further highlights valuable peer recognition and strengthens the department’s standing within the broader academic community.

3.10. Domain: Rewards

• KPI Statement: Number of faculty members received rewards for the year.  

  =COUNTA(UNIQUE(FILTER(F2:F21500,(F2:F21500<>"")*(D2:D21500=2024))))

  This KPI is computed on the same sheet that contains the combined data. The code must be changed if the computation is to be performed on a different sheet.

3.11. Domain: Assessment of Student’s Progress Through Academic Advisor Records

Let us say we need to assess the progress of students at the end of the 2024–2025 academic year. In this case, the academic year is projected to begin in September 2024 and conclude in July 2025.

• KPI Statement: Number of graduated students in the concerned year. Let us obtain the number of total graduated students, irrespective of their enrollment year, at the end of the academic year 2025. This can be measured through  

  =COUNTIFS(I3:I22000, "Graduated", J3:J22000, "July-August-September",     K3:K22000, "2025")+ COUNTIFS(I3:I22000, "Graduated", J3:J22000, "     January",K3:K22000, "2025")

• KPI Statement: Completion rate. The completion rate is calculated based on the minimum program duration for graduation. For a program with a four-year duration, the completion rate represents the percentage of students who graduated within four years or less, relative to the total number of students initially enrolled in that cohort.

  (i)

  TN = Total number of enrollments in the years 2021–2022. This can be determined by using =COUNTIFS(G3:G22000, "2021").

  (ii)

  N = Number of graduated students at the end of the academic year 2024–2025 who enrolled in 2021–2022; this can be filtered by using  

  =COUNTIFS(G10:G22007, "2021",I10:I22007, "Graduated", J10:J22007,      "July-August-September",K10:K22007, "2025")+ COUNTIFS(G10:     G22007, "2021",I10:I22007, "Graduated", J10:J22007, "January",     K10:K22007, "2025")

  Thus, the completion rate is given by the following formula:

  $$\begin{array}{c}\hfill \mathrm{Completion}\mathrm{rate}={\displaystyle \frac{N}{TN}}\times 100.\end{array}$$
• KPI Statement: First-year students retention rate. The first year in the program is very crucial for a student. Based on their performance in this initial year, students generally decide to either move to another program or withdraw from the program. We have to check how many students withdrew from the program who enrolled in the academic year of 2024–2025.  

  =COUNTIFS(G10:G22007, "2024",I10:I22007, "Withdrawn", J10:J22007,     "July-August-September",K10:K22007, "2025")+ COUNTIFS(G10:G22007,     "2024",I10:I22007, "Withdrawn", J10:J22007, "January", K10:K22007,      "2025")

4. Further Discussion and a Comparative Study

In the field of educational accreditation, institutions utilize various tools to monitor key performance indicators (KPIs) effectively. Google Sheets serves as a versatile and user-friendly platform for data management. However, it is important to carefully evaluate its efficiency, security, and scalability in comparison to specialized data management tools [18,19]. This analysis can help institutions make informed decisions about their data management strategies.

4.1. General Comparison with KPI Tracking Software

Several software are useful for KPI tracking (https://clickup.com/blog/kpi-software/ (accessed on 15 February 2025)) in academic institutions, for example, IT-Blocks Higher Education Key Indicators System (https://it-blocks.com/Universities_KPIs.aspx (accessed on 15 February 2025)) and Tableau (https://www.tableau.com/) (accessed on 15 February 2025).

4.1.1. Efficiency

Google Sheets is accessible and allows for real-time collaboration, making it efficient for teams. However, it lacks advanced analytics and automation features found in dedicated tools like Tableau or Microsoft Power BI. These specialized tools provide robust data visualization and analytical capabilities, enabling more complex KPI tracking.

4.1.2. Security

Google Sheets benefits from Google’s robust security infrastructure, which includes encryption and access control. However, dedicated tools often provide enhanced security features tailored specifically for institutional needs. These specialized tools may offer stricter access controls and comply more thoroughly with educational data regulations, making them more suitable for handling sensitive data [20].

4.1.3. Scalability

Google Sheets is capable of managing a moderate amount of data effectively. However, its performance may decline when dealing with larger datasets commonly found in large institutions. On the other hand, dedicated data management tools are specifically designed to handle vast amounts of data efficiently, offering improved performance and scalability for institutions with thousands of students and faculty members.

4.2. Integration of Google Sheets Automation with LMSs and ERP Systems

Integrating automation in Google Sheets with Learning Management Systems (LMSs) and Enterprise Resource Planning (ERP) systems can significantly improve efficiency by streamlining data collection, minimizing manual errors, and providing real-time analytics.

4.2.1. Integration with Learning Management Systems (LMSs)

Google Sheets provides various automation tools such as Google Apps Script, Zapier, and Make, which allow for seamless integration with LMS platforms like Moodle, Blackboard, Canvas, and Google Classroom.

• Automating KPI Tracking for Student Performance: Google Sheets can retrieve real-time student data—such as attendance, grades, and assessment scores—from the Learning Management System (LMS) using API connectors. By utilizing formulas, pivot tables, and Google Apps Script, it can automatically analyze trends and provide instant insights into key performance indicators (KPIs) without the need for manual data entry. For example, a university can track student retention rates by directly pulling enrollment data from platforms like Moodle or Blackboard [21].
• Auto-Syncing Assessment Data for Accreditation Reports LMS-generated grade books can be linked to Google Sheets, where automated scripts calculate pass rates, mean scores, and progression rates for accreditation compliance. Institutions can set up scheduled scripts to pull weekly reports and send alerts for KPI deviations.

4.2.2. Google Sheets and ERP Integration for Institutional Efficiency

Enterprise Resource Planning (ERP) systems like SAP (https://cloud.google.com/integration-connectors/docs/connectors/saperp/configure (accessed on 15 February 2025), Oracle PeopleSoft (https://docs.oracle.com/en/database/oracle/sql-developer-web/sdwad/install.html#GUID-ECBE4F57-73FB-44E6-BF97-6873E6CE0E57 (accessed on 15 February 2025), and Workday (https://marketplace.workday.com/en-US/apps/423827/google-sheets-integration-via-datablend/overview, (accessed on 15 February 2025)) manage faculty workload, financials, HR data, and student records. Google Sheets can act as an intermediary tool for quick data visualization and automation.

4.2.3. Synchronizing Faculty Workload and Budget Data

Google Sheets can pull real-time HR data from ERP, tracking faculty workload per department. Automated reports can compare faculty hours with student enrollment trends, helping optimize resource allocation. For example, an institution using SAP or Workday ERP can automate payroll KPI tracking in Google Sheets, reducing human error and reporting delays [22,23].

4.2.4. Automating Accreditation Compliance Reports

Google Sheets can aggregate data from both LMS and ERP, ensuring all KPI metrics for accreditation (student–faculty ratios, program completion rates, financial data) are stored and analyzed in one place.

4.3. Challenges of Using Google Sheets for KPI Tracking

Although Google Sheets is a popular tool for KPI tracking, several challenges and limitations arise, especially in large institutions with thousands of students and faculty members.

4.3.1. Speed

As the volume of data increases, Google Sheets may experience latency and performance issues. Large datasets can lead to slower load times, making real-time tracking less effective. Dedicated KPI tracking tools are optimized for handling large volumes of data and provide faster performance.

4.3.2. Data Security

Google Sheets relies on Google’s security protocols, which, while generally robust, may not meet the specific security needs of all educational institutions. Concerns regarding data privacy and compliance with regulations can make some institutions wary of using Google Sheets for sensitive data.

4.3.3. Access Control

Managing access permissions in Google Sheets can be cumbersome. While it offers sharing features, ensuring appropriate access levels for thousands of users can become complex. Dedicated tools often provide more sophisticated user management systems, allowing for granular control over data access and editing rights.

4.4. Recommendations in the Context of Current Study

While Google Sheets can be an effective tool for tracking key performance indicators (KPIs), institutions should be aware of its challenges in terms of speed, data security, and access control, especially when dealing with large datasets. Its limitations in efficiency, security, and scalability, compared to dedicated tools, should be carefully considered, particularly in the context of educational accreditation. Despite all the limitations, the proposed mechanism in this study has an independent significance in terms of KPI calculation for program accreditations. Here, we list a few of them.

• Key Differences in Accreditation: Accreditation typically takes place at both the program level (https://etec.gov.sa/en/service/accreditation/servicedocuments, (accessed on 15 February 2025) and institution level (https://etec.gov.sa/en/service/Institutional-Accreditation/servicedocuments, (accessed on 15 February 2025)), each serving important roles in quality assurance. At the program level, the required documentation can vary significantly based on the specific field of study. For instance, key performance indicators (KPIs) for programs in computer science or engineering are tailored differently compared to those for mathematics or physics. This diversity allows for a more focused evaluation of each program’s effectiveness.
  On the other hand, institutional accreditation provides a broader framework without such specific restrictions. Consequently, utilizing local data can be highly beneficial for program accreditation, as it often offers insights that institutional databases may not capture. This targeted approach can greatly enhance the preparation of program-level KPI reports, ensuring they accurately reflect the unique strengths and needs of each program.
• Administrative Hassle in Accessing Institutional Data for Accreditation: Obtaining data from an institution’s data management system for program-level accreditation can be a cumbersome process. Institutional databases are often designed for broad administrative and academic purposes, making it difficult to extract specific data relevant to individual programs. The following challenges may follow in data retrieval:

  –

  Bureaucratic Procedures: institutions may have strict policies regarding data access, requiring multiple approvals before any information is shared.

  –

  Data Aggregation Issues: institutional databases often store data in a generalized format, making it less useful for program-specific accreditation needs.

  –

  Confidentiality and Security: data protection policies can restrict access to certain datasets, delaying the accreditation process.

  –

  Inconsistent Data Formats: the format and structure of institutional data may not align with the specific requirements of program-level key performance indicators (KPIs).

  –

  Time-Consuming Process: administrative delays, multiple stakeholders, and a lack of real-time data availability can slow down the preparation of program accreditation reports.

  Program-specific data are more accurate and tailored to meet accreditation requirements. They reduce reliance on institution-wide data management, thereby minimizing administrative challenges. By collecting localized data, programs can monitor their progress in real-time and make informed decisions for improvement. Therefore, depending on local data sources instead of institutional databases is often a more efficient method for preparing KPI reports for program accreditation.
• Accessibility during urgent needs: Accreditation reviewers may request immediate data verification or additional reports. Local data are significantly more efficient for urgent accreditation needs compared to institutional data. They allow for quick access, customization, and program-specific insights, which ensures a smooth and responsive accreditation process. Institutions should prioritize local data management strategies to improve preparedness and reduce administrative delays.
• Reduced Dependency on IT and Administration: Institutional databases typically require IT support for access, modifications, and report generation. In contrast, local data allow academic departments to manage their own accreditation data. This independence streamlines processes and decreases reliance on IT, leading to a more efficient and proactive approach to data oversight.
• Essential Information Collection: The proposed method in this study carefully refrains from requesting highly confidential information, such as financial transactions and the identities of faculty and students, including their transcripts. Instead, it focuses on gathering information that is generally shareable and essential for the accreditation process. This approach promotes transparency and collaboration within the academic community.
• Reduced security concerns: The data collection process is meticulously designed to ensure security, fostering a direct one-to-one relationship between individual faculty members and the designated sheet owner, typically the head of the accreditation unit or a relevant committee leader. The master collection sheet serves as a comprehensive repository for consolidating information across specific categories and is exclusively managed by the sheet owner, guaranteeing both integrity and confidentiality.
  Upon collection, the data are seamlessly mapped to automatically calculate key performance indicators (KPIs). This finalized dataset is then promptly shared with the designated departmental committee to prepare a thorough analysis report of the KPIs, which will be used for accreditation purposes.
  With the robust security measures that Google has implemented, this system effectively safeguards sensitive information from unauthorized access, ensuring the minimum required protection.
• Support to institutional accreditation unit: There are several KPIs for institutional accreditation that closely align with the KPIs or data at the program level. To strengthen the connection between institutional and program-level performance, institutional quality units can effectively use their structured Quality Management System to gather relevant information as needed. This data collection can include raw data or comprehensive analysis reports, offering flexibility based on specific requirements.
  By implementing effective calculation methods to measure key institutional KPIs, these units can gain valuable insights. This process, which utilizes detailed micro-level information from each program, not only enhances the understanding of performance but also promotes continuous improvement across the institution.

5. Conclusions

In summary, Google Sheets presents a valuable opportunity for higher education institutions to effectively measure key performance indicators (KPIs) in a cost-effective and user-friendly manner. Its strengths lie in its accessibility, real-time collaboration, and customization, making it a practical choice for many. Nonetheless, there are areas for growth, particularly in scalability, security, and advanced analytics. By addressing these limitations, Google Sheets has the potential to become an even more robust tool for institutions focused on optimizing KPI tracking and fostering data-driven decision-making. With thoughtful adaptations, this tool can significantly contribute to continuous improvement and enhanced accountability in meeting accreditation standards.

Furthermore, we recognize that institutions or departments with adequate financial resources and specialized expertise can harness advanced institutional software, such as Learning Management Systems (LMSs) or Enterprise Resource Planning (ERP) systems, by exploring their integration with the automation processes outlined here.