Knowledge Tracing (KT) enables adaptive learning systems to offer personalized learning experiences by modeling student learning behaviors. While recent studies in KT have shown promising results with attention-based models, the performance impact of the attention mechanism has not been sufficiently explored. To fill this gap, we aim to investigate the effects of different attention mechanisms in KT models. Through extensive experiments, we assess the strengths and limitations of five attention mechanisms across three benchmark datasets. Our results indicate that Talking-Heads Multi-Head attention outperforms the other four attention mechanisms, with the most significant improvements observed in terms of AUC, with an average performance increase exceeding 3% over the next best-performing method.