Developing Academic Practice: Volume 2021, Issue March

2021 ◽  
Vol 2021 (March) ◽  
10.28945/4246 ◽  
2019 ◽  

[This Proceedings paper was revised and published in the 2019 issue of the Journal of Information Technology Education: Innovations in Practice, Volume 18.] Aim/Purpose: The study examined types of errors made by novice programmers in different Java concepts with students of different ability levels in programming as well as the perceived causes of such errors. Background: To improve code writing and debugging skills, efforts have been made to taxonomize programming errors and their causes. However, most of the studies employed omnibus approaches, i.e. without consideration of different programing concepts and ability levels of the trainee programmers. Such concepts and ability specific errors identification and classifications are needed to advance appropriate intervention strategy. Methodology: A sequential exploratory mixed method design was adopted. The sample was an intact class of 124 Computer Science and Engineering undergraduate students grouped into three achievement levels based on first semester performance in a Java programming course. The submitted codes in the course of second semester exercises were analyzed for possible errors, categorized and grouped across achievement level. The resulting data were analyzed using descriptive statistics as well as Pearson product correlation coefficient. Qualitative analyses through interviews and focused group discussion (FGD) were also employed to identify reasons for the committed errors. Contribution:The study provides a useful concept-based and achievement level specific error log for the teaching of Java programming for beginners. Findings: The results identified 598 errors with Missing symbols (33%) and Invalid symbols (12%) constituting the highest and least committed errors respec-tively. Method and Classes concept houses the highest number of errors (36%) followed by Other Object Concepts (34%), Decision Making (29%), and Looping (10%). Similar error types were found across ability levels. A significant relationship was found between missing symbols and each of Invalid symbols and Inappropriate Naming. Errors made in Methods and Classes were also found to significantly predict that of Other Object concepts. Recommendations for Practitioners: To promote better classroom practice in the teaching of Java programming, findings for the study suggests instructions to students should be based on achievement level. In addition to this, learning Java programming should be done with an unintelligent editor. Recommendations for Researchers: Research could examine logic or semantic errors among novice programmers as the errors analyzed in this study focus mainly on syntactic ones. Impact on Society: The digital age is code-driven, thus error analysis in programming instruction will enhance programming ability, which will ultimately transform novice programmers into experts, particularly in developing countries where most of the software in use is imported. Future Research: Researchers could look beyond novice or beginner programmers as codes written by intermediate or even advanced programmers are still not often completely error free.


Author(s):  
Lennart E. Nacke

This chapter presents the physiological metrics used in Games User Research (GUR). Aimed at GUR professionals in the games industry, it explains what methods are available to researchers to measure biometric data while subjects are engaged in play. It sets out when it is appropriate to use biometric measures in GUR projects, the kind of data generated, and the differing ways it can be analysed. The chapter also discusses the trade-offs required when interpreting physiological data, and will help games researchers to make informed decisions about which research questions can benefit from biometric methodologies. As the equipment needed to collect biometric data becomes more sophisticated as well as cheaper, physiological testing of players during a game’s development will become more common. At the same time, Games User Researchers will become more discriminating in its use. Where in the past professionals in the games industry have used biometric testing to generate quick, actionable feedback about player responses to elements of a game, and have been less concerned with the scientific robustness of their methodology, as GUR develops a new breed of games industry professionals are attempting to deploy good academic practice in their researches.


Religions ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 222
Author(s):  
Elaine M. Fisher

This article makes the case that Vīraśaivism emerged in direct textual continuity with the tantric traditions of the Śaiva Age. In academic practice up through the present day, the study of Śaivism, through Sanskrit sources, and bhakti Hinduism, through the vernacular, are generally treated as distinct disciplines and objects of study. As a result, Vīraśaivism has yet to be systematically approached through a philological analysis of its precursors from earlier Śaiva traditions. With this aim in mind, I begin by documenting for the first time that a thirteenth-century Sanskrit work of what I have called the Vīramāheśvara textual corpus, the Somanāthabhāṣya or Vīramāheśvarācārasāroddhārabhāṣya, was most likely authored by Pālkurikĕ Somanātha, best known for his vernacular Telugu Vīraśaiva literature. Second, I outline the indebtedness of the early Sanskrit and Telugu Vīramāheśvara corpus to a popular work of early lay Śaivism, the Śivadharmaśāstra, with particular attention to the concepts of the jaṅgama and the iṣṭaliṅga. That the Vīramāheśvaras borrowed many of their formative concepts and practices directly from the Śivadharmaśāstra and other works of the Śaiva Age, I argue, belies the common assumption that Vīraśaivism originated as a social and religious revolution.


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