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In today’s dynamic situation, innumerable challenges are posited in the education sector because of the COVID-19 pandemic. Higher educational institutes (HEIs) are compelled to adopt digital technologies and technology-mediated learning in the teaching-learning processes. The purpose of this paper is to understand the factors affecting learning effectiveness, learning satisfaction and the mediating role of prerecorded videos from the learners’ perspective.

A self-designed structured questionnaire based on previous similar studies is adopted as a survey instrument. It consists of 22 questions to address the five constructs of the proposed hypothesized conceptual model, developed for the study. Data of 311 students from HEIs of Maharashtra state in India were collected. Confirmatory factor analysis is carried out to test the model fitness, reliability and validity, and structural equation modeling is applied to conduct path analysis and hypotheses testing of the model.

Hypotheses testing reveals that perceived usefulness (PU) significantly affects the perceived learning effectiveness, which again affects the learning satisfaction of the students. In addition, perceived ease of use affects the PU as suggested in the technology acceptance model. The prerecorded videos have a moderating role to play in the computer self-efficacy and the perceived learning effectiveness of the students. This research will provide meaningful acumen to enhance the overall learning process among students in urban as well as rural India.

This study explores the technology-mediated learning during the unexpected and dynamic situations of the COVID-19 pandemic in the context of higher education in India. For sustainable use of technology-assisted learning, educators must understand the key factors that influence students’ learning effectiveness and satisfaction. The research outcomes will lead toward developing the human capacities, as the prerecorded videos at the HEIs of India will provide new approaches for effectively adopting digital technologies and technology-mediated learning.

Course evaluations are formative and are used to evaluate learnings of the students for a course. Anomalies in the evaluation process can lead to a faulty educational outcome. Learning analytics and educational data mining provide a set of techniques that can be conveniently applied to extensive data collected as part of the evaluation process to ensure remedial actions. This study aims to conduct an experimental research to detect anomalies in the evaluation methods.

Experimental research is conducted with scientific approach and design. The researchers categorized anomaly into three categories, namely, an anomaly in criteria assessment, subject anomaly and anomaly in subject marks allocation. The different anomaly detection algorithms are used to educate data through the software R, and the results are summarized in the tables.

The data points occurring in all algorithms are finally detected as an anomaly. The anomaly identifies the data points that deviate from the data set’s normal behavior. The subject which is consistently identified as anomalous by the different techniques is marked as an anomaly in evaluation. After identification, one can drill down to more details into the title of anomalies in the evaluation criteria.

This paper proposes an analytical model for the course evaluation process and demonstrates the use of actionable analytics to detect anomalies in the evaluation process.