To increase student achievement, education programs began purchasing computer-assisted instruction (CAI) and integrated learning system (ILS) software starting in the 1950s and 1960s (Aparicio et al., 2016; Bailey, 1992; Becker, 1993; Brush et al., 1999; Watson & Watson, 2007). Since the 1980s, schools have spent millions of dollars on computer software and ILSs to supplement classroom instruction (Bailey, 1992). Today, ILSs function as adaptive software programs that “generate problems, adjust the difficulty and sequence of problems based upon student performance, and provide appropriate and immediate feedback” (Bailey, 1992, p. 4). Instruction is individualized and personalized for each student, and instant data helps teachers target specific areas for growth. Programs such as Achieve 3000, iStation, Imagine Math, iReady, and Dreambox currently offer adaptive programs in reading and math (Estill, 2019). Watch the following video to learning more about adaptive software:

Results from the literature show that the integration of teacher-led classroom activities with computer-based activities such as ILSs leads to successful student outcomes (Akgunduz & Akinoglu, 2017; Becker, 1993; Brown & Warschauer, 2006; Brush et al., 1999; Fassbender & Lucier, 2014; Magana & Marzano, 2014; Staker et al., 2011; Staker & Horn, 2012; Waters & Marzano, 2006). However, teachers play a crucial role in the effectiveness of ILS integration with regard to student achievement especially when teachers are able to identify and individualize instruction by selecting specific activities for students to complete (Becker, 1993; Brush et al., 1999; Fassbender & Lucier, 2014). According to Fassbender and Lucier (2014), “utilizing specialized assignments breeds confidence in learners as higher rates of individualization correlate to higher rates of achievement” (p. 26). Diagnostic data of students in blended learning classrooms where face-to-face activities were coordinated with ILS activities showed higher performance on reading and math assessments than students not using ILS software (Bingham, 2017; Brush et al., 1999).

The following image shows several computer-generated formative assessment tools to help students with the learning process and on-going self-assessment: 

Figure 1. 20 Formative Assessment Tools, https://shakeuplearning.com/blog/20-formative-assessment-tools-for-your-classroom/

References

Akgündüz, D., & Akınoğlu, O. (2017). The impact of blended learning and social media- supported learning on the academic success and motivation of the students in science education. Egitim Ve Bilim, 42(191). Retrieved from https://seu.idm.oclc.org/login?url=https://search-proquest-com.seu.idm.oclc.org/docview/1940828185?accountid=43912

Aparicio, M., Bacao, F., & Oliveira, T. (2016). An e-learning theoretical framework. Journal of Educational Technology & Society, 19(1), 292-307. Retrieved from https://seu.idm.oclc.org/login?url=https://search-proquest- com.seu.idm.oclc.org/docview/1768612693?accountid=43912

Bailey, G. D. (1992). Wanted: A road map for understanding Integrated Learning Systems. In G. D. Bailey (Ed.), Computer-based Integrated Learning Systems (pp. 3-9). Englewood Cliffs, NJ: Educational Technology Publications.

Becker, H. J. (1993). A model for improving the performance of Integrated Learning Systems. In G. D. Bailey (Ed.), Computer based integrated learning systems (pp. 11-31). Englewood Cliffs, NJ: Educational Technology Publications.

Bingham, A. J. (2017). Personalized learning in high technology charter schools. Journal of Educational Change, 18(4), 521-549. Retrieved from http://dx.doi.org.seu.idm.oclc.org/10.1007/s10833-017-9305-0

Brown, D., & Warschauer, M. (2006). From the university to the elementary classroom: Students’ experiences learning to integrate technology in instruction. Journal of Technology and Teacher Education 14(3), 599-621.

Brush, T. A., Armstrong, J., Barbrow, D., & Ulintz, L. (1999). Design and delivery of integrated learning systems: Their impact on students achievement and attitudes. Educational Computing Research, 21(4), 475-486.

Estill, L. (2019). Teachers' perceptions of leadership support for the implementation of learning management systems in urban middle schools. Ann Arbor, MI: ProQuest.

Fassbender, W. J., & Lucier, J. A. (2014). Equalizing the teacher-to-student ratio through technology: A new perspective on the role of blended learning. Voices from the Middle, 22(2), 21-28. Retrieved from https://seu.idm.oclc.org/login?url=https://search- proquest-com.seu.idm.oclc.org/docview/1635286461?accountid=43912

Magana, S., & Marzano, R. J. (2014). Enhancing the art and science of teaching with technology. Bloomington, IN: Solution Tree Press.

Staker, H., Chan, E., Clayton, M., Hernandez, A., Horn, M. B., & Mackey, K. (2011). The rise of K-12 blended learning: Profiles of emerging models [White paper]. Retrieved from Innosight Institute: https://www.christenseninstitute.org/wp- content/uploads/2013/04/The-rise-of-K-12-blended-learning.emerging-models.pdf

Staker, H., & Horn, M. B. (2012). Classifying k-12 blended learning. Retrieved from https://files.eric.ed.gov/fulltext/ED535180.pdf

Waters, J. T., & Marzano, R. J. (2006, September). School district leadership that works: The effect of superintendent leadership on student achievement. McRel, 3-25. Retrieved from www.mcrel.org

Watson, W. R., & Watson, S. L. (2007). An argument for clarity: What are learning management systems, what are they not, and what should they become? Tech Trends, 51(2), 28-34. Retrieved from https://seu.idm.oclc.org/login?url=https://search-proquest- com.seu.idm.oclc.org/docview/223124171?accountid=43912