Academic journal article NAER - Journal of New Approaches in Educational Research

Constructing an Instrument with Behavioral Scales to Assess Teaching Quality in Blended Learning Modalities

Academic journal article NAER - Journal of New Approaches in Educational Research

Constructing an Instrument with Behavioral Scales to Assess Teaching Quality in Blended Learning Modalities

Article excerpt

1 INTRODUCTION

The incorporation of Information and Communication Technologies (ICT) into the sphere of education has represented a turning point in how teaching processes are approached. One of the most significant changes prompted by the application of ICT in education is the appearance of blended learning modalities.

Blended learning modalities, which combine face-to-face learning aspects with others typical of online models, have experienced an enormous growth in recent years, becoming an established trend in the higher education context (Park, Yu, & Jo, 2016).

Many studies have focused on the challenges posed by the implementation of this teaching modality (Garcia-Ruiz, Aguaded, & Bartolome-Pina, 2017; Porter, Graham, Bodily, & Sandberg, 2016). Among these challenges, the way to evaluate teaching quality has emerged as a key aspect. The assessment of educational quality in online and blended learning contexts differs from that typically applied to assess face-to-face modalities (Vonderwell, Liang, & Alderman, 2007). It seems obvious that this training model has its own distinct attributes, but are current quality assessment instruments able to address the singularities of these modalities?

Systems to assess quality in online and blended learning modalities belong to two large groups: (a) systems which use data mining techniques or educational data mining (EDM) to evaluate course quality; and (b) systems where surveys help obtain students' perception of teacher performance, the so-called "student evaluation of teaching" (SET).

1.1 Quality assessment systems based on EDM

Several reviews show how EDM-based techniques have provided decision-making support in evaluating the quality of online and blended learning courses (Baker & Yacef, 2009; Romero & Ventura, 2010 2013).

Information on student activity, accumulated by the LMS (Learning Management System) utilized in these modalities, includes data such as: interactions in discussion forums (Anaya & Boticario, 2009); the number of course material downloads (Grob, Bensberg, & Kaderali, 2004), the number of course pages visited by the student, and the time spent browsing each one of them (Hwang, Tsai, Tsai, & Tseng, 2008). The storage and quantitative analysis of this massive amount of information form the basis for these quality assessment systems (Ai & Laffey, 2007).

Romero et al. (2004), for example, used evolutionary algorithms and multi-objective optimization techniques to provide the teacher with the knowledge required to improve course effectiveness. Retalis et al. (2006) applied cluster analysis and association rules to obtain information about the learning process, oriented toward course quality assessment. Vranic et al. (2007) analyzed course activity data by means of data mining algorithms, in order to improve certain aspects of the course's educational quality. Vialardi et al. (2008) also used data mining algorithms to provide the teacher with recommendations aimed at improving course design and structure. Likewise, Garcia et al. (2011) developed a system of association rules to show the teacher potential modifications that would help enhance course quality. More recently, Kazanidis et al. (2016) developed a system to evaluate course effectiveness using a two fold data analysis: regression analysis; and archetypal analysis of the activity.

1.2 Quality assessment systems based on SET

SET is the most commonly used means to assess quality in online and blended learning courses (Thomas & Graham, 2017). However, in 2004, Bangert highlighted the inability of the existing systems to address teaching practices in online teaching models. Many authors have developed different systems to measure teaching quality in online and blended learning modalities since then.

Bangert himself has put forward his Student Evaluation of Online Teaching Effectiveness (SEOTE). …

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