BM4708 Managing Hybrid Events ICA 3 | NYP

Assessment Brief

Section 1 Outline: 

This individual assignment accounts for 30% of the overall assessment for the learning unit, BM4708 Managing Hybrid Events (MICE). It covers all topics. It is designed to provide a review of the ICA2 group practical, highlighting key learning points from this learning unit, as well as the application of event data analytics before, during, and after a hybrid MICE event.

Section 2 Learning Outcomes: 

After the completion of this individual assignment, learners will be able to:

• Reflect on the key challenges faced when working as a group for BM4708 ICA2 Group Practical and how they have managed to overcome them.
• Reflect on the relevance of the curriculum and/or lesson delivery to BM4708 Managing Hybrid Events (MICE).
• Apply the power of event data analytics before, during and after a hybrid MICE
  event.

Section 3 Requirements:

Part 1: Individual Key Reflection Report

a. Describe any THREE (3) key challenges faced by your project group and explain how you have managed to overcome each key challenge.
b. Describe any THREE (3) relevant points of the curriculum and/or lesson delivered in BM4708.

Explain your points with THREE (3) supporting examples from any part of the lectures.

Part 2: Research and application

a. Review the usefulness of event data analytics to a hybrid MICE event before, during and after, with relevant supporting examples.

Section 4 Submissions:

Section 4.1 Report Format

A typewritten report between 2.5 to 4 pages excluding cover page and relevant, well-labelled appendices and references/citations – Arial Font size 12
with single spacing must be submitted.

The individual typewritten report should consist of the following parts:
Cover Page Include a cover page with the project title, your full name and admission number.

Part 1: Individual Key Reflection Report 

a. Describe any THREE (3) key challenges faced by your project group and explain how you have managed to overcome each key challenge.
b. Describe any THREE (3) relevant points of the curriculum and/or lesson delivered in BM4708. Explain your points with THREE (3) supporting examples
from any part of E-lectures.

Part 2: Research and application 

a. Review the usefulness of event data analytics to a hybrid MICE event before, during and after, with relevant supporting examples.
b. Language and report 
c. Appendices Sources of research data or any other supporting findings.

Please submit your document in either Word or PDF format through NYP Learning. Management System (LMS). Name all your documents submitted in the format “BM4708 ICA3_Elective Group_Your Full Name_Admin Number”.
For example, BM4708 ICA3_E1_Tina Tay Tin Tin_1234567A

Section 4.2 Submission via NYP LMS Turnitin:

The previous section’s requirements are to be submitted through NYP LMS
Turn in the latest by Mon, 17 Feb 2025, 2359 hours sharp. Turnitin is a cloud-based solution with capabilities in originality checking using their OriginalityCheck tool. OriginalityCheck allows your instructor to monitor assignment submissions and identify potential cases of plagiarism by automatically comparing submissions to an online database of original content. It is the responsibility of the learners to ensure that the matching text highlighted
in the Turnitin similarity report is properly referenced. Follow this Guide and video on how to submit your report in NYP LMS Turnitin.

Section 4.3 Rules and Penalties for Late Submission of Assignments and Project Deliverables:

The following rules and penalties apply to assignments and project deliverables submitted after the due date: Late submissions within 5 calendar days of the due date:

• If the learner passes the assignment/project, the awarded mark will be capped at 50% of the base score for that assignment/project component.
• If the learner fails the assignment/project, a failing score will be awarded for
  that assignment/project component. Late submissions more than 5 calendar days after the due date:
• The learner will receive a score of zero for that assignment/project component, regardless of the work’s quality.

Section 4.4 Plagiarism and Use of Generative Artificial Intelligence (AI): 

As agreed upon in the Academic Integrity Policy Acknowledgement you signed last semester, you must adhere to the principles of academic honesty. Responsible AI Use for Assessment (AI used allowed + Citation) In this learning unit, students have the option to use artificial intelligence (AI)
for assignments based on their assessment of whether it supports their learning objectives. Use of AI is permitted as a learning tool but is not required or expected. Students are trusted to utilise AI ethically and responsibly. However, it is crucial to recognise the limitations of these tools. Students must critically evaluate and verify all AI-generated information, data and facts to ensure accuracy and truthfulness. Students are fully accountable for the integrity of the content they submit.

Citation. If incorporating AI-generated material, students must clearly label it as such. In-text attributions and references should follow APA style guidelines. Presenting AI content as one’s work, fabricating sources, or failing to disclose AI use all constitute academic misconduct and will result in serious disciplinary action. When in doubt, err on the side of over-disclosure of AI use.

Section 5 Assessment Components:

This individual assignment carries a total of marks and constitutes 30% of the overall grade for the module. The breakdown of marks is as follows:

Appendix 1: Scoring Rubrics of Individual Assignment