1 University of Arizona South (UNITED STATES)
2 Big Apple Institute - Brooklyn, New York (UNITED STATES)
About this paper:
Appears in: INTED2010 Proceedings
Publication year: 2010
Pages: 5263-5268
ISBN: 978-84-613-5538-9
ISSN: 2340-1079
Conference name: 4th International Technology, Education and Development Conference
Dates: 8-10 March, 2010
Location: Valencia, Spain
Technology offers a unique opportunity to help students connect their learning to real world problems. With students spending vast amounts of time using computer applications or online additional consideration needs to be devoted to the cognitive level of human-computer interactions (HCIs) that students are experiencing (Arnold & Moshchenko, 2009). For skill learning, all age groups benefit more from active–elaborative training than from passive training, and are more adept at transferring the learned skill to a more difficult task (Vakil, Hoffman, & Myzliek, 1998). When individuals subject stimuli to different levels of mental processing, they retain information that has been subjected to the most thorough processing (Craik & Lockhart, 1972).

Much of the student-computer interactions (SCIs) transpire in the form of transmission of information from the computer to the student as input (Arnold & Moshchenko, 2009). Hunter (2004) refers to input modalities as “channels through which we get information” (p. 6). Student output validates the acquisition of skills and knowledge (p. 7). Having students engage in developing tangible output with technology boosts performance in the discipline it is being used to support (Arnold & Moshchenko, 2009)

There are three kinds of computer use for helping students learn: teaching students about computers, using computers as a teacher, and using computers to assist students in learning (Thorsen, 2009). Educational technology software application includes instructional, productivity, and administrative (Roblyer & Doering, 2010).

In this quasi-experimental study we looked at the learning outcomes associated with varying levels of cognitive stimulation experienced by students using technology. We categorized technology use into two general categories: facilitator of administrative duties and direct student learning support tool. As a student learning support tool we further subcategorized into three modes: demonstration tool, student as user, and student as designer/developer. A comparison among students who were passive viewers of teacher presented information, students who controlled their own information access and students who created a technology enhanced project using the same information resulted in significant differences. Preliminary results indicate the creators of a technology product using the information experienced greater gains in learning.


Arnold, S.D., & Moshchenko, M. (2009). Technology input versus input and output: Does it result in learning differences among elementary school students? In C.D. Maddux (Ed.), Research Highlights in Technology and Teacher Education 2009 (pp. 1-9). Chesapeake, VA: Society for Information Technology & Teacher Education.

Craik, F.I.M., & Lockhart, R.S. (1972). Levels of processing: A framework for memory research. Verbal Learning and Verbal Behavior, 11 (6), 671-684.

Hunter, R. (2004). Madeline Hunter’s mastery teaching: Increasing instructional effectiveness in elementary and secondary schools. Thousand Oaks, CA: Corwin Press.

Roblyer, M.D., & Doering, A.H. (2010). Integrating educational technology into teaching (5th ed.). Upper Saddle River, NJ: Pearson Education, Inc.

Thorsen, C. (2009). Tech tactics: Technology for teachers (3rd ed.). Boston, MA: Pearson Education, Inc.

Vakil, E., Hoffman, Y., & Myzliek, D. (1998). Active versus passive procedural learning in older and younger adults. Neuropsychological Rehabilitation, 8 (1), 31-41.
Technology, use, modes, learning, students, information, transfer, computer.