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This paper identifies antecedents to frustration arising in micro-task crowd work (MTCW) and measures their impact on a frustrating work experience. Surprisingly, research to date has said little on frustration in MTCW, despite it being a central challenge to working persistently in these increasingly important socio-technical work environments. Guided by an experiential computing perspective, we applied a mixed-method approach to reveal how the interplay between platforms and other workers' behavior shapes frustration experienced in MTCW. We present qualitative and quantitative findings on frustration in virtual MTCW. Based on 96 narratives, we qualitatively identify three dominant antecedents to frustration: technical problems in conducting tasks, platform competition and, the inability to disconnect from work. We subsequently conducted a quantitative two-wave survey (N = 181), to test the factors' impacts on MTCW frustration. The results confirm the three factors and additionally reveal that autonomy largely reduces frustration. However, autonomy also reinforces frustration arising from the inability to disconnect from work. Thus, workers' inability to disconnect from work can lure them into excessive availability for taking on additional tasks. Thereby, workers experience additional frustration and underutilize the benefits of autonomy in crowd work. Our manuscript offers three central contributions: First, we identify frustration as pivotal in the experience of MTCW and its antecedents. Second, we shed light on the heterogenous role of autonomy in relation to MTCW frustration. Finally, we link the findings on frustrating MTCW experience to experiential computing. • Identifying antecedents of experienced frustration in micro-task crowd work (MTCW) • Platform competition, technical problems, inability to disconnect spur frustration • Autonomy decreases frustration and eases coping with technical problems • Autonomy reinforces frustration from workers with inability to disconnect from MTCW [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has changed the entire world, while the impact and usage of online learning environments has greatly increased. This paper presents a new version of the ReaderBench framework, grounded in Cohesion Network Analysis, which can be used to evaluate the online activity of students as a plug-in feature to Moodle. A Recurrent Neural Network with LSTM cells that combines global features, including participation and initiation indices, with a time series analysis on timeframes is used to predict student grades, while multiple sociograms are generated to observe interaction patterns. Students' behaviors and interactions are compared before and during COVID-19 using two consecutive yearly instances of an undergraduate course in Algorithm Design, conducted in Romanian using Moodle. The COVID-19 outbreak generated an off-balance, a drastic increase in participation, followed by a decrease towards the end of the semester, compared to the academic year 2018–2019 when lower fluctuations in participation were observed. The prediction model for the 2018–2019 academic year obtained an R 2 of 0.27, while the model for the second year obtained a better R 2 of 0.34, a value arguably attributable to an increased volume of online activity. Moreover, the best model from the first academic year is partially generalizable to the second year, but explains a considerably lower variance (R 2 = 0.13). In addition to the quantitative analysis, a qualitative analysis of changes in student behaviors using comparative sociograms further supported conclusions that there were drastic changes in student behaviors observed as a function of the COVID-19 pandemic. • We evaluate students' behaviors before and during COVID-19 as a plug-in to Moodle. • A RNN combines global features with time series analysis to predict student grades. • Multiple sociograms are generated to observe interaction patterns. • A significant increase in online participation is observed during the pandemic. • The prediction model obtained an R 2 of 0.27 and 0.34 for the two academic years. [ABSTRACT FROM AUTHOR]