HRM233 Training and Development TMA01 July 2026, Singapore

HRM233 Training and Development Tutor-Marked Assignment (TMA01): July 2026

The July 2026 HRM233 TMA01 asks students to act as a junior L&D practitioner and run a Training Needs Analysis (TNA) for a real organisation, using public information only. The report must apply Noe’s (2023) three-level needs assessment framework, recommend a training approach with transfer and Kirkpatrick evaluation in mind, and include an AI Reflection Log on how AI tools were used and checked.

Background

Identifying learning needs before designing any training intervention is a fundamental skill for L&D practitioners. A Training Needs Analysis (TNA) ensures that learning solutions are purposeful, evidence-based and aligned to real organisational goals. In this assignment, you will conduct a TNA for a real organisation of your choice, applying key theoretical frameworks to structure your thinking and justify your approach. You will also use AI tools as part of your practice and critically evaluate what they produce.

Scenario

You are working as a junior L&D practitioner. Your HR Manager has asked you to conduct a needs assessment by identifying a performance gap within the organisation that suggests there is a training need. Your task is to conduct the needs assessment, produce a professional report, create a supporting artefact and maintain an AI Reflection Log documenting how you used AI tools throughout the process.

You may select any real organisation and refer to publicly available information such as annual reports, job advertisements, LinkedIn, news articles and industry data to build an evidence base for the performance gap. You are not required to provide your own organisation’s data.

Part A: Written Report (no more than 1,700 words) [70 marks]

Section 1: Organisational Overview (no more than 200 words) [10 marks]

Briefly introduce the organisation (its size, sector and workforce context), explain why it is relevant and how you accessed information about it.

• The organisation’s industry/sector and approximate size (employees, revenue, geographic reach)
• The sources you used to gather information and any limitations of those sources

Section 2: Identify performance issues that training is needed (no more than 300 words) [10 marks]

Describe the specific gap you have identified: what is currently happening and what should be happening, supported by evidence.

• A clear statement of the performance or knowledge gap, distinguishing it from operational or resourcing problems
• Evidence that the gap exists, linked to at least one external or internal source
• Why addressing this gap matters to the organisation’s goals or strategy

Section 3: The Needs Assessment Process (no more than 600 words) [30 marks]

Explain how you would conduct a thorough needs assessment for this organisation by applying Noe’s (2023) three-level framework, and state any practical constraints (e.g. access to data, time, organisational sensitivity). Address any two of the following:

• Organisational Analysis: how organisational strategy, resources and climate support or constrain training, including the organisation’s readiness for training.
• Task Analysis: how you would identify the KSAOs required for the role or performance area, and suitable methods (job analysis, SME interviews, competency frameworks).
• Person Analysis: how you would determine which employees need training and what they already know, with suitable data-gathering tools (performance appraisals, surveys, observations).

Section 4: Recommended Training Approach (no more than 600 words) [20 marks]

Based on your needs assessment findings, create a training method for addressing the identified gap and justify it using Noe (2023).

• The training method(s) recommended (on-the-job, e-learning, classroom-based, blended) and why they suit this gap and organisation
• Transfer of training: factors that would support or hinder learning being applied on the job, with reference to Noe (2023)
• A brief note on evaluation using Kirkpatrick’s four levels
• Feasibility: cost, time or logistical considerations behind the recommendation
• Optional artefacts in the Appendix (e.g. training plan outline, cost-benefit summary), excluded from the word count

Part B: AI Reflection Log (no more than 300 words) [20 marks]

Reflect critically, in the first person, on how you engaged with AI tools (such as ChatGPT, Claude, Copilot or Gemini) during the assignment, including screenshots of AI-assisted work where relevant.

1. Reflection Question 1: How did you use AI in this assignment? Name the tool(s), the stage and the purpose; if you did not use AI, explain why.
2. Reflection Question 2: What were the benefits and limitations of using AI in this context? Would a senior L&D practitioner trust AI outputs without verification?
3. Reflection Question 3: What does this experience tell you about being an AI-literate L&D professional, and what would you do differently next time?

Part C: General Formatting of the Report [10 marks]

• Organisation (5 marks): meaningful headings and heading levels, tables and figures in APA 7th edition format; do not use question numbers as headings.
• Citations and References (5 marks): APA 7th edition, using credible and original sources (original sources listed in Noe (2023) endnotes and session slides).

Submission and Formatting Requirements

• Submit a single Microsoft Word report to CANVAS via your T/TG group and keep the Submission ID as proof.
• Include a cover page with course title, name and PI number, tutorial group, submission date and the Academic Integrity Declaration Statement (plus the Attribution Table if generative AI was used).
• Name the file CourseCode_AssignmentCode_UserID_FullName.
• Times New Roman, 12 point, 1.5 line spacing, 1-inch margins, justified.
• All submissions are screened by Turnitin; late submissions follow the university’s mark deduction scheme.
• A student self-assessment rubric is provided to review the work against Needs Improvement, Sufficient, Proficient and Exceptional bands.