Agile methodologies are adapted by growing number of software organizations. Agile maturity (also called agility) assessment is a way to ascertain the degree of this adoption and determine a course of action to improve agile maturity. There are a number of agile maturity assessment surveys in order to assess team or organization agility and many of them require no guidance. However, the usability of these surveys are not widely studied. The purpose of this study is to determine available agile maturity self-assessment surveys and evaluate their strengths and weaknesses for agile maturity assessment. An extensive case study is conducted to measure the sufficiency of 22 available agile maturity self-assessment surveys according to the seven expected features: comprehensiveness, fitness for purpose, discriminativeness, objectivity, conciseness, generalizability, and suitability for multiple assessment. Our case study results show that they do not satisfy all of the expected features fully but are helpful in some degree based on the purpose of usage.

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Video clickstream behaviors such as pause, forward, and backward offer great potential for educational data mining and learning analytics since students exhibit a significant amount of these behaviors in online courses. The purpose of this study is to investigate the predictive relationship between video clickstream behaviors and students’ test performance with two consecutive experiments. The first experiment was performed as an exploratory study with 22 university students using a single test performance measure and basic statistical techniques. The second experiment was performed as a conclusive study with 16 students using repeated measures and comprehensive data mining techniques. The findings show that a positive correlation exists between the total number of clicks and students’ test performance. Those students who performed a high number of clicks, slow backward speed or doing backwards or pauses achieved better test performance than those who performed a lower number of clicks, or who used fast-backward or fast-forward. In addition, students’ test performance could be predicted using video clickstream data with a good level of accuracy (Root Mean Squared Error Percentage (%RMSE) ranged between 15 and 20). Furthermore, the mean of backward speed, number of pauses, and number/percentage of backwards were found to be the most important indicators in predicting students’ test performance. These findings may help educators or researchers identify students who are at risk of failure. Finally, the study provides design suggestions based on the findings for the preparation of video-based lectures.