Learning and Instruction

Dear Colleagues,

While models and definitions of intelligence and learning are separate and can be discussed independently, the distinction between the two concepts becomes less clear when it comes to the actual activity of learning. Today, intelligence is often described in terms of reasoning skills, which is an essential component of learning effectiveness and creating new knowledge. Most studies on the relationship between these two concepts find little difference in measures of participants’ intelligence and their ability to learn. This suggests a strong correlation between the two concepts. Technology, big data, learning analytics, machine learning, and artificial intelligence offer new ways and possibilities to enhance our understanding of the concept of learning and overcome the “one-size-fits-all approach” by personalizing education to make learning more effective. In this Special Issue on Learning and Instruction, papers should ideally combine the two phenomena—intelligence on one hand and learning and instruction on the other—using the advantages of technology in education. We welcome papers on:

1. The future of learning and instruction research;
2. The role of reasoning skills in learning–reshaping school learning in the 21st century;
3. The future of personalized learning: artificial intelligence and adaptive learning;
4. The possibilities of using process data to make learning processes visible.

We expect most contributions to deal with primary education, but those that consider secondary or tertiary education are equally welcome. In general, we prefer papers built on an empirical basis. We are particularly interested in submissions that introduce good practices. However, those proposing theoretical developments are also welcome. Studies may apply state-of-the-art methods, such as structural equation modeling, IRT analyses, and latent growth analyses, or employ big data methods, such as data mining, machine learning, or artificial intelligence, as long as these methods and models adequately serve and address the underlying research to enhance our understanding of the complex relation between intelligence on one hand and learning and instruction on the other.

Prof. Dr. Gyöngyvér Molnár
Guest Editor

Manuscript Submission Information

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