ABSTRACT Accurately predicting postgraduate student performance is crucial for effective pedagogical interventions to enhance future learning performance. The significant variability in university subjects and institutions complicates this prediction process. This paper proposes a reliable machine learning model, bEGWOA‐FKNN, to address these challenges by predicting student performance based on previous academic records, thereby facilitating satisfactory graduation. The EGWOA algorithm, which introduced the extended crisscross strategy and guided learning strategy to the whale optimisation algorithm (WOA), is employed to select feature subsets from a dataset collected at Wenzhou University. Its global search capability is validated through extensive experiments, including quality analysis, ablation studies and comparisons with state‐of‐the‐art algorithms. The bEGWOA‐FKNN model, which integrates the binary version of the EGWOA (bEGWOA) algorithm with the fuzzy k‐nearest neighbour, selects the most informative features and achieves superior accuracy compared to several existing feature selection methods.

IEEE 802.11ax, introducing Target Wake Time (TWT) mechanism, was proved as the next generation Wireless Local Area Network (WLAN) technology to improve QoS and QoE in dense scenarios. A novel broadcast TWT mechanism is proposed to save power by leveraging the new capability of uplink Orthogonal Frequency Division Multiple Access (OFDMA) based multiuser transmission. However, if the TWT is not properly scheduled, deteriorated throughput and high power consumption occur because of collisions. This paper investigates several key aspects, such as the number of simultaneously awake stations, the number of eligible random access resource units, and backoff stages, that have great impacts on the throughput and power efficiency. Based on the derived relationship, we further propose a TWT scheduling scheme (TSS) for negotiating Target Beacon Transmission Times (TBTTs) by making decisions on whether accepting the TWT request or not to maximize throughput. Besides, an algorithm on arranging stations to wake up in different beacon slots with appropriate offsets (i.e., first TBTTs) is presented. Simulation results demonstrate the effectiveness in terms of average throughput and power efficiency.