Translations∞

improvement of sophisticated ideas. I will discuss these essay∞, custom essays∞, Bing Search engine∞ cases in light of

• The RNS initiative (2002-2010) now spread to over 100 small rural schools, and 23 school districts have adopted the model. The raison d’etre of the initiative was to enrich the learning environment of rural small schools as an alternative to closing them because of demographic changes. Participatory design is a must but some top-down decisions are taken.
improvement of sophisticated ideas. I will discuss these essay∞, Bing Search engine∞ cases in light of

Thus there are Dissertation∞ activity specific cultural artifacts for students to draw
The upsurge of the low cost laptop market signals the advent of one-to-one (1:1) classrooms – classrooms where Essay∞ every student uses at least one wireless-enabled computing device for learning. How do we prove that 1:1 classes are beneficial for the students and Thesis∞ the teacher? What is our research assignment∞ goal, in terms of the pressing and very hard problem of classroom technology adoption in schools, as indicated by many studies (Paley, 2007; NSF Cyber-Learning Report, 2008)?

improvement of sophisticated ideas. I will discuss these essay∞ cases in light of

The upsurge of the low cost laptop market signals the advent of one-to-one (1:1) classrooms – classrooms where every student uses at least one wireless-enabled computing device for learning. How do we prove that 1:1 classes are beneficial for the students and the teacher? What is our research goal, in terms of the pressing and very hard problem of classroom technology adoption in schools, as indicated by many studies (Paley, 2007; NSF Cyber-Learning Report, 2008)?
Most research on technology supported classroom claims learning improvement. But the majority of such research, I speculate, incurs burden on the teacher. A significant increase of academic achievement and a significant decrease of teacher loading, therefore, are at least two necessary conditions, if not the crux, for the success of technology adoption in classrooms. To address these two conditions, I define:
1:1 Productivity = Student Final Achievement Measurement / Teacher Loading
Raising productivity is the primary reason for many technology inventions, so is for classroom technology. From the above definition, the higher student final achievement measurement is, and the lower the teacher loading is, the higher the teacher productivity will be. The teacher, however, usually will not cut down on their loading, even though their role changes in the 1:1 classroom.
Now, what is our research goal? The work of Benjamin Bloom and his colleagues (1984) hints a ringing goal. When comparing the final achievement measurement of the conventional classes learning the subject matter with about 30 students per teacher with that of the experimental classes adopting mastery learning and one-to-one human tutoring, they found that the experimental classes outperformed the conventional ones by two-sigma: The average student in the experimental classes was above 98% of the students in the conventional classes. Their experiment was conducted over 3-week block of time.
The 2-sigma productivity problem of 1:1 classes is defined to be the same as the Bloom’s, except the inclusion of a condition: The teacher loading cannot exceed that of a conventional class. That is, without additional teacher loading, the final achievement of the 1:1 class outperforms that of the conventional class by 2-sigma. This measurement goal induces a series of benchmarks for advancement, starting from, say, 0.6-sigma, then 0.8-sigma, 1-sigma, 1.2-sigma, and so forth, until reaching to 2-sigma. Note that for teacher loading, it is not clear how to measure. For rigorous measurement of student achievement and teacher loading, experimental period should substantially exceed 3 weeks, the period of Bloom’s experiment.
In this session, I’ll briefly discuss some issues and difficulties of our initial effort in tackling this problem for elementary classes learning math and language art, for example, issue mindset versus system mindset. Also, learning ownership seems to be a noticeable consideration in designing 1:1 classroom activities. Three classes of collaborative learning, from this learning ownership perspective, seem to be appropriate for 1:1 classes.

• Collaborative learning/knowledge building in synchronous/asnchronous, verbal and written forms was at the heart of the design of the remote networked school (RNS) in Francophone Quebec, Canada.
• The RNS initiative (2002-2010) now spread to over 100 small rural schools, and 23 school districts have adopted the model. The raison d’être of the initiative was to enrich the learning environment of rural small schools as an alternative to closing them because of demographic changes. Participatory design is a must but some top-down decisions are taken.
• Knowledge Forum and iVisit are the basic collaborative tools, and data is collected on each of the servers dedicated to these tools.
• Idea improvement, which is at the center of Bereiter and Scardamalia’s perspective on collaborative knowledge building, is emphasized during onsite/online teacher professional development activities. However, Knowledge Forum (KF) basic functionalities are taught before the 12 knowledge building principles. The use of KF advanced functionalities are taught as the research-intervention team refers to the knowledge building principles.
• The research-intervention team is present online during and after classtime, and respond to various queries. As the year progresses, queries become more pedagogically-focused.
• Classroom-based learning/knowledge building (Francophone) artefacts are available at http://www.eer.qc.ca/projets/∞
• It is our understanding that for classroom-based collaborative learning and knowledge building to sustain and scale, a number of actors need to take action at the different levels of a specific educational system.

The upsurge market of the low cost laptops has already signaled the advent of one-to-one (1:1) classrooms – classrooms where every student is using at least one wireless-enabled computing device for their learning. But how do we prove that such classes are beneficial for the students as well as for the teacher? What should be our research goal of 1:1 classes, in terms of the pressing but very hard problem of technology adoption in school classrooms, as indicated by many studies?
Most research on technology supported classroom claims on learning improvement. The majority of such work, I speculate, incurs teacher loading. Therefore, a significant increase of academic performance and a significant decrease of teacher loading, I argue, are at least the two necessary conditions, if not the crux, for the success of technology adoption in classrooms, especially classrooms in schools (Chan, 2007, 2008). To address these two conditions, I define:
Productivity, indeed, is the primary reason why human invents technology. From the above definition, the higher student final achievement measurement is and the lower the teacher loading is, the higher the teacher productivity will be. Teachers, however, usually will not reduce their loading, but keep the same amount, even though their role in the classroom might have changed.
Now, what is our research goal? The work of Benjamin Bloom and colleagues (1984) hints a ringing goal. When comparing the final achievement measurement of a conventional class learning a particular subject matter with about 30 students per teacher with that of another class adopting mastery learning and one-to-one human tutoring, they found that the experimental class outperformed the conventional class by two-sigma: The average student in the experimental class was above 98% of the students in the conventional class. Their experiment was conducted over 3-week block of time.
Following Bloom, I define the 2-sigma problem of 1:1 classes is the same as the Bloom’s, with an additional condition: the teacher loading cannot exceed that of a conventional class. The significance of this 2-sigma goal is that it induces a series of advancement benchmarks, from, say, 0.6-sigma, 0.8-sigma, 1-sigma, 1.2-sigma, and so forth, until 2-sigma. For teacher loading, currently, it is not clear how to measure. But experimental period should exceed 3 weeks, the period of Bloom’s experiment.

Most research on technology supported classroom claims improvement on learning performance. The majority of such research, I speculate, incurs teacher loading, however. Therefore, I argue that a significant increase of academic performance and a significant decrease of teacher loading are at least the two necessary conditions, if not the crux, for the success of technology adoption in classrooms, especially classrooms in schools (Chan, 2007, 2008). To address these two conditions, I define:
Indeed, productivity is the primary reason why human creates technology. From the above definition, the higher student final achievement measurement is and the lower the teacher loading is, the higher the teacher productivity will be. Teachers, however, usually will not reduce their loading, but keep the same amount, even though their role might have changed.
Now, what is our research goal? The work of Benjamin Bloom and colleagues (1984) hints a ringing research goal. When comparing the final achievement measurement of a conventional class learning a particular subject matter with about 30 students per teacher with that of another class adopting mastery learning and one-to-one human tutoring, they found that the experimental class outperformed the conventional class by two-sigma: The average student in the experimental class was above 98% of the students in the conventional class. Their experiment was conducted over 3-week block of time.
Following Bloom, the 2-sigma problem of 1:1 classes is the same as the Bloom’s, with an additional condition: the teacher loading cannot exceed that of a conventional class. The significance of this 2-sigma goal is that it induces a series of advancement benchmarks, from, say, 0.6-sigma, 0.8-sigma, 1-sigma, 1.2-sigma, and so forth, until 2-sigma. For teacher loading, currently, it is not clear how to measure. But experimental period should exceed 3 weeks, the period of Bloom’s experiment.
In this session, I’ll briefly discuss some difficulties of tackling this problem for conducting 1:1 classroom research for an elementary class in learning math and language art, in particular, the issue of system mindset vs. issue mindset. Also, the issue of learning ownership seems, to us, to be a noticeable consideration in designing 1:1 learning activities. From this perspective, I’ll describe three classes of collaborative learning that I think may be appropriate for 1:1 classrooms.

From this definition, the higher student final achievement measurement is and the lower the teacher loading is, the higher the teacher productivity will be. Teachers, however, usually will not reduce their loading, but keep the same amount, even though their role might have changed. Now, what is our research goal?

The work of Benjamin Bloom and colleagues (1984) hints a ringing research goal. When comparing the final achievement measurement of a conventional class learning a particular subject matter with about 30 students per teacher with that of another class adopting mastery learning and one-to-one human tutoring, they found that the experimental class outperformed the conventional class by two-sigma: the average student in the experimental class was above 98% of the students in the conventional class. Their experiment was conducted over 3-weeks block of time.
Now, following Bloom, I define the 2-sigma problem of 1:1 classes as the same of the Bloom’s 2-sigma problem, but with an additional condition: the teacher loading cannot exceed that of a conventional class. The significance of this 2-sigma goal is that it induces a series of advancement benchmarks, from, say, 0.6-sigma, 0.8-sigma, 1-sigma, 1.2-sigma, and so forth, until 2-sigma. For teacher loading, currently, it is not clear how to measure. But obviously experimental period should exceed 3 weeks, the period of Bloom’s experiment.
I’ll briefly discuss some difficulties of tackling this problem for conducting 1:1 classroom research for an elementary class in learning math and language art, in particular, the issue of system mindset vs. issue mindset. Also, the issue of learning ownership seems, to us, to be a noticeable consideration in designing 1:1 learning activities. From this perspective, I’ll describe three classes of collaborative learning that I think may be appropriate for 1:1 classrooms.

The Two-Sigma Problem of One-to-One Classes
Today, the upsurge market of the low cost laptops has signaled the advent of one-to-one (1:1) classrooms – classrooms where every student is using at least one wireless-enabled computing device for their learning. But how do we prove that such classes are beneficial for the students and the teacher? What should be our research goal of such 1:1 classes, in terms of the pressing but very hard problem of technology adoption in conventional classes indicated by many researchers?
Most research on technology supported classroom claims improvement on learning performance. The majority of such research, I speculate, incurs teacher loading, however. Therefore, I argue a significant increase of academic performance and a significant decrease of teacher loading are the crux or at least the two necessary conditions for the success of technology adoption in classrooms, especially classrooms in schools (Chan, 2007, 2008). To address these two conditions, I define:
Teacher Productivity = Student Final Achievement Measurement / Teacher Loading
From this definition, the higher the teacher productivity is, the higher student final achievement measurement and the lower the teacher loading will be. The teacher, however, usually will not reduce his/her loading, but is happy to keep the same amount. Now, what is our research goal?
The work of Benjamin Bloom and colleagues (1984) hints a ringing research goal. They compared the final achievement measurement of a conventional class learning a particular subject matter with about 30 students per teacher with that of another class adopting mastery learning and one-to-one human tutoring, the average student in the later class was above 98% of the students in the conventional class. Their experiment was conducted over 3-weeks block of time.
I define the two-sigma problem of 1:1 classes as the same of the Bloom’s two-sigma problem, with an additional condition: the teacher loading cannot exceed that of a conventional class. This 2-sigma goal induces a series of benchmarks of advancement, from, say, 0.6-sigma, 0.8-sigma, 1-sigma, 1.2-sigma, and so forth, until 2-sigma. For teacher loading, currently, it is not clear how to measure. But certainly experiment period should exceed more than 3 weeks, Bloom’s experiment period.
I’ll briefly discuss some difficulties of tackling this problem for conducting 1:1 classroom research for primary class in learning math and language art, in particular, the issue of system mindset vs. issue mindset. Also, the issue of learning ownership seems, to us, to be a noticeable consideration in designing 1:1 learning activities. From this perspective, I’ll describe three classes of collaborative learning that I think may be appropriate for 1:1 classrooms.

examples of a model we have developed that has successfully promoted
students’ engagement in online conversations (chats & conferences), which
has helped to deepen their understanding physics concepts. More
importantly, this model demonstrates collaborative work involving the
improvement of sophisticated ideas. I will discuss these cases in light of
what they tell us about the practical nature of implementing knowledge
building in higher education.
My thinking on the "model" is a systemic one that includes the situation
(the preceding classroom work), which helps to ground the online activity.
Thus there are activity specific cultural artifacts for students to draw
on as they improve their ideas (data, collective memories, etc.).

There are many challenges to designing collaborative knowledge building
activities for college level physics. In this session I will present two
examples of a design model we have developed that has successfully
promoted students’ engagement in online conversations (chats &
conferences), which has helped to deepen their understanding physics
concepts. More importantly, this model demonstrates collaborative work
involving the improvement of sophisticated ideas. I will discuss these
cases in light of what they tell us about the practical nature of
implementing knowledge building in higher education.

The Two-Sigma Problem of One-to-One Classrooms:

The Two-Sigma Problem of One-to-One Classrooms
From individual learning vs.

Testing ...by Chan

Opening statement by Marion Barfurth