The study was carried out to evaluate some sesame varieties under different nutrient levels for enhancing the productivity of sesame during March – June, 2014. The experiment was carried out in a split-plot design with three replications. The main -plot treatments had four nutrient levels viz., 75% of the recommended dose of fertilizer(RDF), 100% RDF, 125% of RDF, and 150% of RDF, and the sub - plot treatments included six sesame varieties viz., Laltil (Local), Atshira (Local), T6, BARI Til-3, BARI Til-4 and Binatil-2. RDF indicates a nutrient schedule of 56:72:23 kg N, P2O5, and K2O ha-1. The effect of nutrient levels, varieties, and their interaction showed significant variation in respect of yield contributing parameters, yield, and harvest index. Results revealed that in nutrient levels, 100% of RDF produced the highest seed yield (1223 kg ha-1). The least seed yield was observed with 150% of RDF (924 kg ha-1). Among the sesame varieties,BARI Til-4 showed the optimum growth and yield contributing parameters as a result highest seed yield (1170 kg ha-1). The lowest seed yield was obtained from Laltil (811.30 kg ha-1). The interaction effect was found significant where highest seed yield of 1481 kg ha-1 with 100% of RDF combination of sesame var. BARI Til-4. Bangladesh Agron. J. 2021, 24(2): 31-41

<div><p class="abstract">This paper reviews the theoretical foundations and components of blended learning (BL) in higher education globally, analyzing six articles from five countries published between January 2016 and December 2020. The study identified challenges faced by instructors, including workload, timeliness, and lack of academic and technical skills to manage BL. Balancing face-to-face and online learning was also challenging. To address these issues, the importance of staff training, support, and networking was emphasized, proposing a modified BL model for tertiary education in Bangladesh, which could be implemented post-pandemic using a machine-learning approach. The mixed BL model was recommended for Bangladeshi institutions, utilizing machine learning algorithms to facilitate outcome-based learning through technological applications. A preliminary survey of 120 students from BGC Trust University in Bangladesh was conducted using statistical data obtained from machine learning algorithms to explore the applicability of the mixed-learning approach. Machine learning proved beneficial for data analysis, drawing valuable insights for educators and policymakers seeking effective teaching strategies that incorporate technology. This research underscores the potential of machine learning in conducting surveys and analyzing data related to blended learning in tertiary education, offering significant contributions to the field.</p></div>