Arnon Hershkovitz

Learning about Online Learning Processes and Students' Motivation through Web Usage Mining

This study illustrates the potential of applying Web usage mining - the analysis of Web log files in educational research. It consists of two sub-studies and focuses on two types of analysis, both related to the whole learning process: investigating one learners activity in order to learn about her or his learning process, and examining the activity of a large group of learners, in order to develop a log-based motivation measure. Subjects were 674 adults who used an online learning unit as part of their preparations for the Psychometric Academic Entrance Exam and whose log files were drawn. The first sub-study aimed to illustrate the knowledge about the online learner that can be extracted from log files, and this resulted in a list of computable, non computable, and higher-level learning variables. In the second sub-study, a log-based motivation measuring tool was developed on the basis of a theoretical framework, a mechanism for computing relevant learning variables, and a clustering of these variables into three groups (associated with the theoretical framework). A discussion of the results, in the context of educational Web mining, is provided.

Discovery With Models A Case Study on Carelessness in Computer-Based Science Inquiry

In recent years, an increasing number of analyses in learning analytics and educational data mining (EDM) have adopted a “discovery with models” approach, where an existing model is used as a key component in a new EDM or analytics analysis. This article presents a theoretical discussion on the emergence of discovery with models, its potential to enhance research on learning and learners, and key lessons learned in how discovery with models can be conducted validly and effectively. We illustrate these issues through discussion of a case study where discovery with models was used to investigate a form of disengaged behavior (i.e., carelessness) in the context of middle school computer-based science inquiry. This behavior was acknowledged as a problem in education as early as the 1920s. With the increasing use of high-stakes testing, the cost of student carelessness can be higher. For instance, within computer-based learning environments, careless errors can result in reduced educational effectiveness, with students continuing to receive material they have already mastered. Despite the importance of this problem, it has received minimal research attention, in part because of difficulties in operationalizing carelessness as a construct. Building from theory on carelessness and a Bayesian framework for knowledge modeling, we use machine-learned detectors to predict carelessness within authentic use of a computer-based learning environment. We then use a discovery with models approach to link these validated carelessness measures to survey data to study the correlations between the prevalence of carelessness and student goal orientation.

Student-teacher relationship in the Facebook era: the student perspective

Student-teacher relationships are vital for students’ academic development and for their well-being. As social network sites (SNS) have become increasingly popular, school authorities and policymakers are concerned about the implications of student-teacher connections using them, often resulting in banning such communication. While these regulations might have far-reaching social impacts, empirical evidence supporting them is meager. The main goal of the qualitative exploratory study reported herewith, involving lower and higher secondary school Israeli students (N = 11), is to understand the relations between Facebook-based student-teacher communication and student-teacher relationships. Findings suggest that communication between Israeli students and teachers on Facebook was limited; however, it was useful for some students as an available means of communication with their teachers. Generally, students see Facebook as a closed territory for youngsters; still, they do tend to befriend teachers they connect with in ‘real-life’. Findings highlight opportunities, rather than risks, of SNS-mediated student-teacher relationships, implicating the need for evidence-based decision-making.

Predicting Robust Learning with the Visual Form of the Moment-by-Moment Learning Curve.

We present a new method for analyzing a students learning over time for a specific skill: analysis of the graph of the students moment-by-moment learning over time. Moment-by-moment learning is calculated using a data-mined model that assesses the probability that a student learned a skill or concept at a specific time during learning (Baker, Goldstein, & Heffernan, 2010, 2011). Two coders labeled data from students who used an intelligent tutoring system for college genetics. They coded in terms of 7 forms that the moment-by-moment learning curve can take. These labels are correlated to test data on the robustness of students’ learning. We find that different visual forms are correlated with very different learning outcomes. This work suggests that analysis of moment-by-moment learning curves may be able to shed light on the implications of students’ different patterns of learning over time.

Modeling and studying gaming the system with educational data mining

In this chapter, we will discuss our work to understand why students game the system. This work leverages models of student gaming, termed “detectors”, which can infer student gaming in log files of student interaction with educational software. These detectors are developed using a combination of human observation and annotation, and educational data mining. We then apply the detectors to large data sets, and analyze the detectors’ predictions, using discovery with models methods, to study the factors associated with gaming behavior. Within this chapter, we will discuss the work to develop these detectors, and what we have discovered through these analyses based on these detectors. We will discuss evidence for how gaming the system impacts learning and evidence for why students choose to game. We will also discuss attempts to address gaming the system through adaptive scaffolding.

Carelessness and goal orientation in a science microworld

In this paper, we study the relationship between goal orientation within a science inquiry learning environment for middle school students and carelessness, i.e., not demonstrating an inquiry skill despite knowing it. Carelessness is measured based on a machine-learned model. We find, surprisingly, that carelessness is higher for students with strong mastery or learning goals, compared to students who lack strong goal orientation.

Teacher-Student Relationship and SNS-Mediated Communication: Perceptions of Both Role-Players.

Teacher-student relationships are vital for academic and social development of students, for teachers’ professional and personal development, and for having a supportive learning environment. In the digital age, these relationships can extend beyond bricks and mortar and beyond school hours. Specifically, these relationships are extended today while teachers and students communicate via social networking sites (SNS). This paper characterizes differences between teachers (N=160) and students (N=587) who are willing to connect with their students/teachers via Facebook and those who do not wish to connect. The quantitative research reported here within is based on data collection of personal characteristics, attitudes towards Facebook, and perceptions of teacher-student relationship. Findings suggest differences in characteristics of the two groups (willing to connect vs. not willing to connect) within both populations (teachers and students). Also, in both populations, those who were willing to connect, compared to those who were not willing to connect, present more positive attitudes towards using Facebook for teaching/learning and are more opposed to a banning policy of student-teacher SNS-based communication. We also found that students who were willing to connect showed a greater degree of closeness with their teachers compared to those who were not willing to connect. This study may assist policymakers when setting up regulations regarding teacher-student communication via social networking sites.

Genealogy as a lifelong learning endeavor

ABSTRACT Genealogy has gained much popularity in recent years as a lifelong learning endeavor, usually conducted by adults during their leisure time. So far, only little is known about the educational aspects of genealogy. In this study, we define six characteristics of genealogy as lifelong learning: harmonious passion, suddenly triggered; implementation of research practices; inductive and deductive relationships with multiple disciplines; technology as a research partner; consuming information and producing knowledge in multiple representations; and strengthening intergenerational connections – present, past and future. This set of characteristics is based on a qualitative study of active genealogists (N = 8) through in-depth interviews. We discuss these features and their implications on learning and teaching genealogy to various audiences.

The Interplay between Affect and Engagement in Classrooms Using AIED Software

Affect has been hypothesized to play a significant role in triggering engagement/disengagement during learning. In this paper, we study the inter-relationships between students’ affect (boredom, confusion, frustration, engaged concentration) and their engaged and disengaged behaviors (off-task, on-task solitary, on-task conversation, gaming the system). We study these relationships in the context of four different software programs, involving students of different ages, in order to increase confidence in the generalizability of the findings. Understanding these relationships might assist in maintaining students’ engagement over time.

Teachers’ perceptions of out-of-class communication, teacher-student relationship, and classroom environment

Communication between students and teachers is usually extended beyond the classroom’s time and space. This communication, referred to as out-of-class communication (OCC), may impact students’ academic, social, and emotional development. Today, OCC is facilitated via social media and instant messaging services, which may affect its nature. This quantitative study (N = 155) aims to analyze the impact of WhatsApp™-based OCC between middle- and high-school teachers and their students on two variables that are key to learning and teaching: Teacher-student relationship and classroom environment. The data was collected through teachers’ self-reported questionnaires. Overall, we find that despite manifested somewhat differently from traditional OCC, teachers’ use of WhatsApp for OCC is also associated with better relationship with students and with better classroom environment.