Comparison of Online Learning Behaviors in School vs. at Home in Terms of Age and Gender Based on Log File Analysis

Abstract

Introduction The use of ICT in school allows enhancement of teaching and learning beyond the walls of the classroom. This meets a wide range of school-related needs, such as repeat practice for students, access to materials that are not available in school, and so on (Ally, 2004; Picciano & Seaman, 2007). Therefore, many schools around the world have been introducing online courses or blended learning. But whereas the teacher has significant control over students' online learning process in the classroom, outside the classroom the students confront the assignments alone. They have more responsibility for their learning and are required to demonstrate autonomy and independence in learning. The degree of students' control in online learning is reflected in the way they choose to consume the content: the path, the pace, and the duration of learning, as well as the effort they are willing to invest (Beck, 2007; Sims & Hedberg, 1995). This will yield different patterns of learning. Previous research showed that factors like gender (McSporran & Young, 2001) and age (Cavanaugh, Gillan, Kromey, Hess, & Blomeyer, 2004) are correlated with certain aspects of online learning behaviors. Moreover, several studies showed a need for young students to be scaffolded as part of the distance learning experience; only by the time they are enrolled in higher education may they acquire a degree of autonomy in learning (Cavanaugh, 2005). Many schools, therefore, are concerned about their students' quality of learning and level of readiness for autonomous online learning (Picciano & Seaman, 2007). Hence, there is a need to enhance our understanding of learning behaviors, especially how learners proceed with and without teacher presence, i.e., at school and at home. However, when working with a large population, learners' online behaviors are not easily traced with traditional research methodologies, such as questionnaires and observations (Nachmias & Hershkovitz, 2007). Analysis of the data documented in the log file can help us with this type of mission. While they are learning in online environments, students actually leave a continuous trace of their activity in the form of log file records, which document every action taken with reference to three parameters: what was the action taken, who took it, and when. Extracting these actions from the log files and analyzing them can yield meaningful information and new insights about how students learn in online environments and about their learning styles and needs (Pahl, 2004; Romero & Ventura, 2007). Our aim in this study is to analyze the data derived from students' log files in order to learn more about online learning processes at home and in school, and also to explore if there are certain behavioral differences in both locations in terms of age and gender. Background Online Learning Online learning in K-12 has become more widespread in the past decade. According to a 2008 report by the U.S. Department of Education, the number of K-12 public education students who enrolled in technology-based distance education grew by 65% between 2002-03 and 2004-05 (Zandberg & Lewis, 2008). Picciano and Seaman (2007) estimated that more than a million K-12 students took online courses in the 2007-2008 school years. One of the most valuable benefits of integrating ICT in school practice is that it enhances the possibilities of teaching and learning beyond the traditional limitations of time and space (Arbelaiz & Gorospe, 2009; Tubin, Mioduser, Nachimias, & Forkosh-Baruch, 2003). Our previous research (Ben-Zadok, Leiba, Nachmias & Mintz, 2009) revealed that many schools in Israel exploit this advantage by using the online environment for home tasks and independent learning. Much research has been done regarding the contribution of online and blended learning. Results point out, for example, that such learning offers contents not otherwise available at school, that it meets the needs of specific groups of students, and that it reduces scheduling conflicts (Picciano & Seaman, 2007). …