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Welcome to the 9 emerging trends in managing awards, grants, scholarships, abstracts and more.

Webinar Highlights:

Reference Collection: Moving beyond traditional reference letters to more efficient methods. Benefits for both nominators and nominees.
Enhancing Transparency: Sharing feedback with nominees to improve their experience. Building trust through a transparent evaluation process.
Normalize Results: Identifying trends in judging to ensure fair outcomes. Normalizing results for consistency. Redacting personal information.
Dual Nominator/Nominee Workflow: Streamlining the nomination process to include input from both nominators and nominees. Enhancing collaboration and accuracy in nominations.
Data Security: Best practices for protecting nominee information. Ensuring compliance with data protection regulations.
AI Content Detection: Leveraging AI to detect AI-generated content in nominations. Ensuring the authenticity and integrity of submissions.

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Unlock the emerging trends in award, grant, and scholarship management

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By submitting you agree to our terms

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Dual Nominator/Nominee Workflow

The Dual Nominator/Nominee Workflow is structured to optimize both participation and data accuracy in award, scholarship, or grant applications. Initially, nominators submit basic information about themselves and the nominee. This first step is crucial as it allows for a high volume of entries by reducing the barrier to entry, crucial for initial engagement. It also brings awareness to the program by potential self applicants who were just not aware. Opposed to the “nominator” sending them a link to apply, organizations now have valuable data and insights into who’s been nominated for automated email follow ups and conversion metric tracking. In the second phase, nominees themselves are prompted to provide a detailed and comprehensive account of their qualifications and fill out the full application. This puts the ownership on the applicant themselves.This two-tiered approach ensures:

High Participation: Simple initial nominations encourage more entries as well as the ability to monitor conversions and boost participation through automated reminders.
Rich Data Collection: Detailed applications from nominees allow for a thorough evaluation based on substantial and self-verified information.
Consistent Selection: With only the data submitted directly by the applicant we can ensure all applicants are being evaluated fairly and not based off deliverables submitted by others about them.
Streamlined Processing: Separating casual entries from serious applications early in the process helps manage resources more efficiently

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Increase nominations, collect more accurate data, and engage participants by leveraging a duel nominator/nominee workflow.

Collect basic info from the nominator and then collect the more robust information directly from the nominee.

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Let's get started!

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Lower the barrier for nominators by keeping things simple.

a) Who is the nominator

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B) Who are you nominating?

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And C), why are you nominating them?

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Easily allow nominators to continue submitting nominations and further boost participation.

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Once the nomination gets submitted either:

a) manually trigger the next step for nominees or,

b) automatically notify the nominee of the nomination and allow them to begin submitting their information.

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After receiving the notification email the nominees will join the nomination and be presented with nominee specific questions.

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Leverage the nomination form submitted by the nominee as a way to gather current and up to date records such as contact information, employer, etc.

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Capture the powerful data necessary for review committees to conduct evaluations and make informed decisions. Note: you have full control over what content is captured - this is demo text and queston prompts.

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Trigger a notification to potential references to submit either reference letters or reference template forms. These deliverables submitted by the reference will be automatically attached to the applicants profile.

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Supporting over 130 file types, collect essential documents such as pdf's, spreadsheets, images, videos, and more. Ensure data security and compliance with secure document management and file access rights.

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Reference Collection

The revamped method of reference collection focuses on a standardized approach, ensuring consistent, reliable, and relevant information from each reference. By transitioning from narrative-based reference letters to a structured questionnaire format, the system achieves several key improvements:

Avoid reference letters:

Historically references were collected in a letter format but this is now an outdated and risky method.
Letters create a barrier for references.
- Hard to write
- Takes time and effort
- Multi-step to create, write, and send back.
- Not all references are created equal – some are better written than others, some had more time put into it, etc. Is this a fair representation of the scholarship applicant?
- Hard to blind PII in a letter.

Leverage reference templates:

Outline 3-5 questions that each reference should answer.
Consider adding a reference evaluation scoresheet.
Lowers the time and effort barrier for references.
Creates data consistency amongst all applicants.
Creates consistency in the review process with defined data sets.

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Within the submission form ask the applicant to provide the name and email of the reference.

From here, an email trigger will be sent directly to the reference with a link to fill out the reference template

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Automatically trigger a notification to the reference and direct them to submit the reference template.

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After clicking the link within the email notification the reference is brought directly into the reference template to submit.

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As references are being collected operation teams can monitor in real time how many references have been requested, progress, access the deliverables, and generate automated reminders for completion.

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When viewing an applicants profile we can see who the requested references are...

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The status of the request which is critical for follow ups and automation

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Open the line item for the supplemental form you wish to view (in this case, the reference).

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And after clicking view on the reference itself we can see the contents of the entire submitted reference embedded directly within Reviewr.

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Still collecting file uploads, no worries - easily view the deliverable directly within Reviewr.

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And don't worry, you can still view traditional reference letters embedded directly within Reviewr for ease of access.

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AI/Plagiarism Detection

As artificial intelligence (AI) continues to evolve, the need to distinguish between human-generated and AI-assisted content has become increasingly important. This distinction is crucial in settings where originality and authenticity are highly valued such as in awards, grants, scholarships, call for speakers, abstract collection, fellowships, and more. The implementation of AI content detection tools is aimed at identifying the extent to which AI has been used in the preparation of submissions. These Reviewr tools are able to differentiate between content that is entirely generated by AI and content that has been used AI to assist the human creator. In a similar fashion, it’s also critical to identify and plagiarized material as well as the identified source from where the content was derived.

The AI content detection system integrates several functionalities:

Detection and Analysis: It can precisely identify the presence of AI-generated text, providing a percentage score that indicates the extent of AI or plagiarised content involvement.
Maintaining Integrity: By flagging submissions that heavily rely on AI, the system ensures that the contributions reflect the true capabilities and efforts of the applicants. This is essential for maintaining the integrity of competitive academic and professional environments.
Policy Enforcement and Education: Understanding the role of AI in submissions allows institutions to enforce existing policies or develop new guidelines regarding the use of AI technologies. It also provides an educational opportunity to inform participants about acceptable practices related to AI use.

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From the submission listing menu easily see at a glance what percentage of the submission was assisted with the usage of A.I. tools such as ChatGPT

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When viewing the content of an applicants submission, deep dive into A.I. detected content within a submission on a question-by-question basis.

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Get a detailed report of flagged content for A.I. assistance from popular tools such as ChatGPT.

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Alongside the A.I. detection score you will find a plagiarism detection score. From here, deep dive into the results as well as the matched source of the identified plagiarism.

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Highlighted is the flagged plagiarism detection results

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The matched detection indicates where the content was found and the source.

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Fair, Non-Bias, Selection Process

Creating a fair, non-biased framework is essential for any evaluation process, especially those involving diverse applicant pools such as scholarships, awards, and grants. Reviewr employs several strategies to ensure fairness and objectivity:

Randomized Review Assignments: Reviewr can randomly assign applications to reviewers. This method helps prevent any preconceived notions or biases that reviewers might have towards certain candidates, promoting a more equitable evaluation process. There are many ways to structure a review and selection workflow that can still rely on a form of randomization, if if a workflow consists of phases, committees, categories, etc.
- They key component to randomization of the submission to evaluator pairing is to first identify the workload capacity of reviewers and then from here, identify how many times each individual submitter must be reviewed to get a proper gauge on the quality of the application. For example, we may decide that review teams should not evaluate more than 20 people due to time constraints, but at the same time, each applicant must get reviewed by 5 different evaluators. Within Reviewr, we can just enter these metrics and the system will auto generate the assignments.
Anonymization of Submissions: To further prevent bias, Reviewr can redact any identifying information from applications. This ensures that evaluations are based solely on the merits of the content and the qualifications of the candidates, not on any personal or demographic characteristics. Any information that is also not relevant to the review and selection process such as data collected for record keeping purposes, even if not PII, should also be redacted to lower the barrier for the selection team and ensure a seamless experience.
Transparency in Processes: The organization provides clear guidelines and criteria for evaluations, which are openly communicated to all participants. This transparency helps build trust in the fairness of the process and ensures that all candidates understand how decisions are made.

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Easily create workflows and pair up applicants with review teams that meet your unique needs.

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Control access by filling up the workflow bucket with review staff. Staff will only haver access to applicants assigned to them.

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Normalize Results

To ensure fairness across different evaluators who may have varying standards of strictness, normalizing results is a critical process. This involves adjusting scores to a common scale and calibrating the assessments across different reviewers. This process reduces the impact of biases and ensures consistent evaluations, no matter who conducts them.

Let us tell you a story. An ivy league university that we will not mention year over year would have evaluators question why particular candidates were not selected. Those that reviewed the applicant thought they were strong fits and actually one of the highest they evaluated. For the sake of privacy, let’s pretend this evaluators name was Bob. Bob, who never scores an applicant greater than 15 evaluated a particular candidate as a 14. Bob later was shocked to hear that this applicant was not selected as it was the highest rated applicant Bob scored. Looking into the data, it was later identified that because not all applicants were evaluated by all reviewers, that some evaluators were scoring people as 25s consistently. While that particular candidate was scored “highly” by Bobs standards, it was actually quite low compared to the standards of other evaluators. This lies the problem - how do we not only identify judging trends by particular evaluators, but also take this into account when generating results.

Standardization Techniques: Implementing statistical methods to adjust scores based on the average stringency or leniency of reviewers. Reviewers normalization report will identify each particular judges average score amongst all the applicants they review, and then compare that overall average against a particular candidate to see if that applicant is higher, or lower, than how that judge normally scores. We can then create a baseline of “average” and use this averaged baseline in comparison to other judges and applicants.
Calibration Sessions: Regular meetings where reviewers discuss and align on scoring standards to ensure consistency in how evaluation criteria are applied.
Feedback Loops: Incorporating feedback from both reviewers and candidates to continuously refine the scoring and normalization processes.

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Reviewr's normalized results helps identify outlier judges who score higher, or lower, than the average judge. Identifying if evaluators are just more strict than others helps avoid penalizing applicants who may have been more harshly reviewed than others.

Here we see a list of applicants alongside each individual judge that scored them on a per question basis.

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We can now identify what the average each individual judges scores

This judge on average gives applicants a score of 23ish.

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This judge on average gives applicants a score of 34. Wow, what a difference... and one that would dictate the results of the program and potentially life changing opportunities for the applicant.

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Ok, so what do we do?

We normalize the results by taking the scores submitted by the judge and giving any applicant that scores that judges average a 1. Anything higher than 1 means it was scored better than average, and anything lower than 1 is lower than average.

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Measuring Impact

Measuring the impact of programs such as scholarships, grants, and awards is crucial for understanding their effectiveness and for reporting to stakeholders. This involves a systematic approach to collecting and analyzing data to assess whether the program's objectives are being met. Key aspects of this process include:

Long Term Tracking: Monitoring the progress of recipients over time to evaluate the long-term impact of the program. This helps in understanding how effectively the program contributes to the career or educational advancements of its beneficiaries.
Outcome-Based Metrics: Establishing specific, measurable outcomes that the program aims to achieve. These could include academic success, professional achievements, community impact, or other relevant metrics depending on the program's goals.
Regular Reporting: Providing periodic reports to stakeholders, which detail the outcomes and successes of the program. This transparency is essential for maintaining trust and support from donors, participants, and the public.
Feedback Collection: Gathering insights from participants and other stakeholders through surveys, interviews, and other feedback mechanisms. This information is invaluable for refining the program and addressing any areas where it may not be meeting expectations.

Ultimately, impact and grant reporting is not a challenge - but collecting the proper data from applicants to generate these reports is. It’s critical that we collect in a timely, seamless manner, this data that can be easily recalled for generating reports.

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Before we can get to a comprehensive dashboard with KPI's for community and stakeholder impact reporting we must first create an ecosystem of data collection from selected applicants throughout their journey/program lifecycle.

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Selected applicants can return to their profile and add additional deliverables, triggered by workflows. This is where the applicants can provide ongoing data collection for reporting purposes.

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Applicants will select the type of deliverable they are submitting/being requested

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After selecting the deliverable type the applicant will be dropped directly into the form to submit the deliverables.

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Once submitted, the contents of the report/deliverables can be viewed directly within Reviewr.

The content from these reports/deliverables can then be leveraged for organization wide program success reporting.

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Transparency & Data Security/Privacy

Enhancing transparency in the process of selection and evaluation is key to building trust among participants and stakeholders. Transparency involves clear communication about the processes, criteria, and decisions made during the evaluation and selection phases. Here’s how transparency is integrated:

Clear Criteria and Processes: Detailed documentation of all criteria and processes used in the selection and evaluation stages is made available to all participants. This ensures that everyone understands the basis on which decisions are made.
Accessibility of Information: Making information about the process easily accessible to all candidates, including potential applicants and unsuccessful candidates, helps demystify the process and reduces perceptions of bias or unfairness.
Real-Time Updates: Providing participants with real-time updates on their status within the program, as well as any changes to the process or criteria. This ongoing communication helps keep participants informed and engaged.
Post-Selection Feedback: Offering detailed feedback to participants after the selection process, especially to those who were not selected. This can provide valuable insights into areas for improvement and encourage future participation.
Build community trust and create an environment for self development and growth.
- Did we follow the Proof of Process (POP)
  - Sets the stage to ensure a fair, non-bias, trustworthy, and compliant program
  - Public facing statement that outlines the entire submission application and selection process that Ultimately can be mapped back to show the process was followed.
  - Elements of a Proof of Process (POP)
    - Dates and deadlines
    - Outline of the submission process
    - Outline of the review and selection process
    - Post selection process
- If an applicant was not selected, are we prepared to prove why?
Compliance with global standards

In an ever-changing and dynamic environment, compliance standards must be constantly monitored.
- GDPR, FERPA, California Personal Information Act, etc
- Leave this to the exports (Reviewr).
- SOC2 Type 2 Certification
  - Industry gold standard for data security and privacy
  - While particular tools may be compliant (form builders, association management softwares, etc), they may not be when utilized together to power a program they were unintended for.

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Applicants when returning to their profile can take action on it depending on statuses, workflows, and desired outcomes by operation teams.

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Open shared evaluations, results, comments, etc. Have direct control over exactly what, and when, you want to share content to applicants.

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Control what content is made available to applicants and how much transparency should be provided.

Show scores, comments, and overall results - this is up to the program management team.

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Applicants can easily click view to access the entire shared scorecard, or elements of the scorecard, such as comments.

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Opening a submitted evaluation shows the elements of the selection sheet that program teams wish to share back to applicants.

Again, share the entire scorecard, elements of it, just the comments - this is up to you!

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Learn More

Interested in learning more about incorporating these trends into your programs or want to learn more about what your program would look like in Reviewr? Use the button below to schedule a 1-on-1 consultation with Reviewr.

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