Purpose: This study aims to evaluate the effectiveness of three large language models (LLMs), GPT-4o, Llama 3.3 70B and Llama 3.1 8B, in redacting personally identifying information (PII) from forum data in massive open online courses (MOOCs). Design/methodology/approach: Forum posts from students enrolled in nine MOOCs were redacted by three human reviewers. The GPT and Llama models were then tasked with de-identifying the same data set using standardized prompts. Discrepancies between LLM and human redactions were analyzed to identify patterns in LLM errors. Findings: All models achieved an average recall of over 0.9 in identifying PII and identified PII instances overlooked by humans. However, their precisions were lower -- 0.579 for GPT-4o, 0.506 for Llama 3.3 and 0.262 for Llama 3.1 -- showing a tendency to over-redact non-PII names and locations. Research limitations/implications: Several courses' data were analyzed to increase findings' generalizability but the models' performance may vary in other contexts. GPT and Llama models were selected because of their availability and cost-effectiveness at the time of the study; future newer models may improve performance. Practical implications: The use of downloadable LLMs enables researchers to de-identify data without training specialized models or involving external companies, ensuring that student data remains private. Originality/value: Previous research on LLM text de-identification has largely used proprietary models, which require sharing data containing sensitive PII with third-party companies. This study evaluates the performance of two open weight models that can be deployed locally, eliminating the need to share sensitive data externally.